Fig.2.1. Simple and compound modules.
OMNeT++ Discrete Event Simulation System
OMNeT++ is an object-oriented modular discrete event simulator. The name itself stands for Objective Modular Network Testbed in C++. OMNeT++ has its distant roots in OMNeT, a simulator written in Object Pascal by dr. György Pongor.
The simulator can be used for modeling:
An OMNeT++ model consists of hierarchically nested modules. The depth of module nesting is not limited, which allows the user to reflect the logical structure of the actual system in the model structure. Modules communicate with message passing. Messages can contain arbitrarily complex data structures. Modules can send messages either directly to their destination or along a predefined path, through gates and connections.
Modules can have parameters which are used for three main purposes: to customize module behaviour; to create flexible model topologies (where parameters can specify the number of modules, connection structure etc); and for module communication, as shared variables.
Modules at the lowest level of the module hierarchy are to be provided by the user, and they contain the algorithms in the model. During simulation execution, simple modules appear to run in parallel, since they are implemented as coroutines (sometimes termed lightweight processes). To write simple modules, the user does not need to learn a new programming language, but he/she is assumed to have some knowledge of C++ programming.
OMNeT++ simulations can feature different user interfaces for different purposes: debugging, demonstration and batch execution. Advanced user interfaces make the inside of the model visible to the user, allow him/her to start/stop simulation execution and to intervene by changing variables/objects inside the model. This is very important in the development/debugging setPhase of the simulation project. User interfaces also facilitate demonstration of how a model works.
Since it was written in C++, the simulator is basically portable; it should run on most platforms with a C++ compiler. OMNeT++'s advanced user interfaces support X-window, DOS and are portable to Win3.1/Win95/WinNT.
OMNeT++ has been extended to execute the simulation in parallel. Any kind of synchronization mechanism can be used. One suitable synchronization mechanism is the statistical synchronization, for which OMNeT++ provides explicit support.
OMNeT++ Home Page on the Web:
http://www.hit.bme.hu/phd/vargaa/omnetpp.htm
There are numerous network simulation tools on the market today, both commercial and non-commercial. In this section I will try to give an overview by picking some of the most important or most representative ones in both categories and comparing them to OMNeT++: Parsec, SMURPH, NS, Ptolemy, NetSim++, C++SIM, CLASS as non-commercial, and OPNET, COMNET III as commercial tools. (The OMNeT++ Home Page contains a list of Web sites with collections of references to network simulation tools where the reader can get a more complete list.) In the commercial category, OPNET is widely held to be the state of the art in network simulation. OMNeT++ is targeted at roughly the same segment of network simulation as OPNET.
Seven issues are examined to get an overview about the network simulation tools:
Detail Level. Does the simulation tool have the necessary power to express details in the model? In other words, can the user implement arbitrary new building blocks like in OMNeT++ or he is confined to the predefined blocks implemented by the supplier? Some tools like COMNET III are not programmable by the user to this extent therefore they cannot be compared to OMNeT++. Specialized network simulation tools like NS (for IP) and CLASS (for ATM) also rather fall into this category.
Available Models. What protocol models are readily available for the simulation tool? On this point, non-commercial simulation tools cannot compete with some commercial ones (especially OPNET) which have a large selection of ready-made protocol models. OMNeT++ is no exception.
Defining Network Topology. How does the simulation tool support defining the network topology? Is it possible to create some form of hierarchy (nesting) or only "flat" topologies are supported? Network simulation tools naturally share the property that a model (network) consists of "nodes" (blocks, entities, modules, etc.) connected by "links" (channels, connections, etc.). Many commercial simulators have graphical editors to define the network; however, this is only a good solution if there is an alternative form of topology description (e.g. text file) which allows one to generate the topology by program. OPNET follows a unique way: the network topology is stored in a proprietary binary file format which can be generated (and read) by the graphical editor and C programs linked against a special library. On the other hand, most non-commercial simulation tools do not provide explicit support for topology description: one must program a "driver entity" which will boot the model by creating the necessary nodes and interconnecting them (Parsec, SMURPH, NS). Finally, a large part of the tools that do support explicit topology description supports only flat topologies (CLASS). OMNeT++ probably uses the most flexible method: it has a human-readable textual topology description format (the NED language) which is easy to create with any text-processing tool (perl, awk, etc.), and the same format is used by the graphical editor. It is also possible to create a "driver entity" to build a network at run-time by program. OMNeT++ also supports submodule nesting.
Programming Model. What is the programming model supported by the simulation environment? Network simulators typically use either thread/coroutine-based programming (such as activity() in OMNeT++), or FSMs built upon a handleMessage()-like function. For example, OPNET, SMURPH and NetSim++ use FSMs (with underlying handleMessage()), Parsec and C++SIM use threads. OMNeT++ supports both programming models; the author does not know of another simulation tool that does so.
Debugging and Tracing Support. What debugging or tracing facilities does the simulation tool offer? Simulation programs are infamous for long debugging periods. C++-based simulation tools rarely offer much more than printf()-style debugging; often the simulation kernel is also capable of dumping selected debug information on the standard output. Animation is also often supported, either off-line (record&playback) or in some client-server architecture, where the simulation program is the "server" and it can be viewed using the "client". Off-line animation naturally lacks interactivity and is therefore little use in debugging. The client-server solution typically has limited power because the simulation and the viewer run as independent operating system processes, and the viewer has limited access to the simulation program's internals and/or it does not have enough control over the course of simulation execution. OPNET has a very good support for command-line debugging and provides both off-line and client-server style animation. NetSim++ and Ptolemy use the client-server method of animation. OMNeT++ goes a different way by linking the GUI library with the debugging/tracing capability into the simulation executable. This architecture enables the GUI to be very powerful: every user-created object is visible (and modifiable) in the GUI via inspector windows and the user has tight control over the execution. To the author's best knowledge, the tracing feature OMNeT++ provides is unique among the C++-based simulation tools.
Performance. What performance can be expected from the simulation? Simulation programs typically run for several hours. Probably the most important factor is the programming language; almost all network simulation tools are C/C++-based. Performance is a particularly interesting issue with OMNeT++ since the GUI debugging/tracing support involves some extra overhead in the simulation library. However, in a reported case, an OMNeT++ simulation was only 1.3 slower than its counterpart implemented in plain C (i.e. one containing very little administration overhead), which is a very good showing. A similar result was reported in a performance comparison with a Parsec simulation.
Source Availability. Is the simulation library available in source? This is a trivial question but it immediately becomes important if one wants to examine or teach the internal workings of a simulation kernel, or one runs into trouble because some function in the simulation library has a bug and/or it is not documented well enough. In general it can be said that non-commercial tools (like OMNeT++) are open-source and commercial ones are not. This is also true for OPNET: the source for simulation kernel is not available (although the ready-made protocol models come with sources).
In conclusion, it can be said that OMNeT++ has enough features to make it a good alternative to most network simulation tools, and it has a strong potential to become one of the most widely used network simulation packages in academic and research environments. The most serious shortcoming is the lack of available protocol models, but since this is mostly due to the fact that it is a relatively new simulation tool, with the help of the OMNeT++ user community the situation is likely to become much better in the future.
The manual is organized around the following topics:
TBD
OMNeT++ has been developed mostly by András Varga at the Technical University of Budapest, Department of Telecommunications (BME-HIT). Here's a list of the people who have or had anything to do with OMNeT++:
Author and maintainer of the code:
András Varga BME-HIT : andras@metechnology.com, andras@whale.hit.bme.hu
Advisor/contributor:
György Pongor BME-HIT : pongor@hit.bme.hu
Co-author (old NED compiler), until 1993:
Ákos Kun BME
JAR compiler (now called NEDC), sample simulations; summer 1995:
Jan Heijmans TU Delft
Alex Paalvast TU Delft
Robert van der Leij TU Delft
New feaures, testing, new examples; fall 1995:
Maurits André TU Delft, M.J.A.Andre@twi.tudelft.nl
George van Montfort TU Delft, G.P.R.vanMontfort@twi.tudelft.nl
Gerard van de Weerd TU Delft, G.vandeweerd@twi.tudelft.nl
JAR (NEDC) support for distributed execution; also current user:
Gábor Lencse BME-HIT : lencse@hit.bme.hu
PVM support (as final project), spring 1996:
Zoltán Vass BME-HIT
P2, k-split algorithms and more, from fall 1996:
Babak Fakhamzadeh TU Delft
We have to mention Dr. Leon Rothkranz from the Technical University of Delft whose work made it possible for the Delft students to come to Budapest in 1995.
The starting point of this manual was the 1995 report of Jan Heijmans, Alex Paalvast and Robert van der Leij.
OMNeT++ provides efficient tools for the user to describe the structure of the actual system. Some of the main features are:
An OMNeT++ model consists of hierarchically nested modules which communicate with messages. OMNeT++ models are often referred to as networks. The top level module is the system module. The system module contains submodules, which can also contain submodules themselves (Fig.2.1). The depth of module nesting is not limited; this allows the user to reflect the logical structure of the actual system in the model structure.
Modules that contain submodules are termed compound modules, as opposed simple modules which are at the lowest level of the module hierarchy. Simple modules contain the algorithms in the model. The user implements the simple modules in C++, using the OMNeT++ simulation class library.
Both simple and compound modules are instances of module types. While describing the model, the user defines module types; instances of these module types serve as components for more complex module types. Finally, the user creates the system module as an instance of a previously defined module type; all modules of the network are instantiated as submodules and sub-submodules of the system module.
When a module type is used as a building block, there is no distinction whether it is a simple or a compound module. This allows the user to split a simple module into several simple modules embedded into a compound module, or vica versa, aggregate the functionality of a compound module into a single simple module, without affecting existing users of the module type.
Module types can be stored in files separately from the place of their actual usage. This means that the user can group existing module types and create component libraries. This feature will be discussed later, in chapter 8.
Modules communicate by exchanging messages. In an actual simulation, messages can represent frames or packets in a computer network, jobs or customers in a queuing network or other types of mobile entities. Messages can contain arbitrarily complex data structures. Simple modules can send messages either directly to their destination or along a predefined path, through gates and connections.
The "local simulation time" of a module advances when the module receives a message. The message can arrive from another module or from the same module (self-messages are used to implement timers).
Gates are the input and output interfaces of modules; messages are sent out through output gates and arrive through input gates.
Each connection (also called link) is created within a single level of the module hierarchy: within a compound module, one can connect the corresponding gates of two submodules, or a gate of one submodule and a gate of the compound module (Fig.2.2).
Due to the hierarchical structure of the model, messages typically travel through a series of connections, to start and arrive in simple modules. Such series of connections that go from simple module to simple module are called routes. Compound modules act as 'cardboard boxes' in the model, transparently relaying messages between their inside and their outside world.
Connections can be assigned three parameters which facilitate the modeling of communication networks, but can be useful for other models too:
Each of these parameters is optional. One can specify link parameters individually for each connection, or define link types (also called channel types) once and use them throughout the whole model.
The propagation delay is the amount of time the arrival of the message is delayed by when it travels through the channel. Propagation delay is specified in seconds.
The bit error rate has influence on the transmission of
messages through the channel. The bit error rate is the probability
that a bit is incorrectly transmitted. Thus, the probability that
a message of n bits length is transferred correctly is:
P( sent message received properly ) = (1 - ber)n
where ber = bit error rate and n = number of bits
in message.
The message has an error flag which is set in case of transmission errors.
The data rate is specified in bits/second, and it is used for transmission delay calculation. The sending time of the message normally corresponds to the transmission of the first bit, and the arrival time of the message corresponds to the reception of the last bit:
The above model is not applicable for modeling some protocols like Token Ring and FDDI where the stations repeat the bits of a frame that arrives on the ring immediately, without waiting for the whole frame to arrive; in other words, frames "flow through" the stations, being delayed only a few bits. If you want to model such networks, the data rate modeling feature of OMNeT++ cannot be used.
If a message travels along a route, through successive links and compound modules, the model behaves as if each module waited until the last bit of the message arrives and only start its transmission then:
Since the above effect is usually not the desired one, typically you will want to assign data rate to only one connection in the route.
Modules can have parameters. Parameters are used for three purposes:
Parameters can take string, numeric or pointer values; numeric values include expressions using other parameters and calling C functions, random variables from different distributions, and values input interactively by the user.
Numeric-valued parameters can be used to construct topologies in a flexible way. Within a compound module, parameters can define the number of submodules, number of gates, and the way the internal connections are made.
Compound modules can pass parameters or expressions of parameters to their submodules. Parameter passing can be done by value or by reference.
During simulation execution, if a module changes the value of a parameter taken by reference, the changed value propagates to other modules. This effect can be used to tune the model or as a second means of module communication. Pointer-valued parameters can be used to implement shared memory.
The user defines the structure of the model in NED language descriptions (NEtwork Description).The NED language will be discussed in detail in Chapter 4.
The simple modules of a model contain the algorithms as C++ functions. The full flexibility and power of the programming language can be used, supported by the OMNeT++ simulation class library.
OMNeT++ supports a process-style description method for describing activities. During simulation execution, simple module functions appear to run in parallel, because they are implemented as coroutines (also termed lightweight processes). Coroutines were chosen because they allow an intuitive description of the algorithm and they can also serve as a good basis for implementing other description methods like state-transition diagrams or Petri nets.
OMNeT++ has a consistent object-oriented design. One can freely use OOP concepts (inheritance, polymorphism etc) to extend the functionality of the simulator.
Elements of the simulation (messages, modules, queues etc.) are represented as objects. These classes are part of the simulation class library:
The objects are designed so that they can efficiently work together, creating a powerful framework for simulation programming.
Each simple module type is implemented with a C++ class. Simple module classes are derived from a simple module base class, by redefining the virtual function that contains the algorithm. The user can add other member functions to the class to split up a complex algorithm; he can also add data members to the class.
It is also possible to derive new simple module classes from existing ones. For example, if one wants to experiment with retransmission timeout schemes in a transport protocol, he can implement the protocol in one class, create a virtual function for the retransmission algorithm and then derive a family of classes that implement concrete schemes. This concept is further supported by the fact that in the network description, actual module types can be parameters.
The use of smart container classes allows the user to build aggregate data structures. For example, one can add any number of objects to a message object as parameters. Since the added objects can contain further objects, complex data structures can be built.
There is an efficient ownership mechanism built in. The user can specify an owner for each object; then, the owner object will have the responsibility of destroying that object. Most of the time, the ownership mechanism works transparently; ownership only needs to be explicitly managed when the user wants to do something non-typical.
The forEach mechanism allows one to enumerate the objects inside a container object in a uniform way and do some operation on them. This feature which makes it possible to handle many objects together. (The forEach feature is extensively used by the user interfaces with debugging capability and the snapshot mechanism; see later.)
It most cases, the functionality offered by the OMNeT++ classes is enough for the user. But if it is needed, one can derive new classes from the existing ones or create entirely new classes. For flexibility, several member functions are declared virtual. When the user creates new classes, certain rules need to be kept so that the object can fully work together with other objects.
The class library is designed so that objects can give textual information about themselves. This makes it possible to peek into a running simulation program: through an appropriate user interface, one can examine (and modify) the internal data structures of a running simulation. This feature helps the user to get some insight what is happening inside the model and get hands-on experience.
A unique feature called snapshot allows the user to dump the contents of the simulation model or a part of it into a text file. The file will contain textual reports about every object; this can be of invaluable help at times of debugging. Ordinary variables can also be made to appear in the snapshot file. Snapshot creations can be scheduled from within the simulation program or done from the user interface.
This section gives some idea how to work with OMNeT++ in practice: issues like model files, compiling and running simulations are discussed.
An OMNeT++ model consists of the following parts:
The simulation system provides the following components:
Simulation programs are built from the above components. First, the NED files are compiled into C++ source code, using the NEDC compiler which is part of OMNeT++. Then all C++ sources are compiled and linked with the simulation kernel and a user interface to form a simulation executable.
Running the simulation and analyzing the results
The simulation executable is a standalone program; thus, it can be run on other machines without OMNeT++ or the model files being present. When the program is started, it reads in a configuration file (usually called omnetpp.ini); it contains settings that control how the simulation is run, values for model parameters, etc. The configuration file can also prescribe several simulation runs; in the simplest case, they will be executed by the simulation program one after another.
The output of the simulation is written into data files: output vector files, output scalar files, and possibly the user's own output files. OMNeT++ provides a GUI tool named Plove to view and plot the contents of output vector files. But it is not expected that someone will process the result files using OMNeT++ alone: output files are text files in a format which (maybe after some preprocessing using sed, awk or perl) can be read into math packages like Matlab or its free equivalent Octave, or imported into spreadsheets like Excel. All these external programs have rich functionality for statistical analysis and visualization, and OMNeT++ does not try to duplicate their efforts. This manual briefly describes some data plotting programs and how to use them with OMNeT++.
User interfaces
The primary purpose of user interfaces is to make the inside of the model visible to the user, to start/stop simulation execution, and possibly allow the user intervene by changing variables/objects inside the model. This is very important in the development/debugging setPhase of the simulation project. Just as important, a hands-on experience allows the user to get a 'feel' about the model's behaviour. A nice graphical user interface can also be used to demonstrate how the model works internally.
The same simulation model can be executed with different user interfaces, without any change in the model files themselves. The user would test and debug the simulation with a powerful graphical user interface, and finally run it with a simple and fast user interface that supports batch execution.
Component libraries
Module types can be stored in files separately from the place of their actual usage. This means that the user can group existing module types and create component libraries.
Universal standalone simulation programs
A simulation executable can store several independent models that use the same set of simple modules. The user can specify in the configuration file which model he/she wants to run. This allows one to build one large executable that contains several simulation models, and distribute it as a standalone simulation tool. The flexibility of the topology description language also supports this approach.
To help you navigate among files in the OMNeT++ distribution, here's a list what you can find in the different directories.
The omnetpp directory contains the following subdirectories.
The simulation system itself:
omnetpp/ OMNeT++ root directory
bin/ soft links to executables in src/
lib/ soft links to sim. library files in src/
doc/ manuals (Word6, HTML), readmes, license etc.
src/
nedc/ system description compiler
sim/ simulation kernel
std/ files for non-distributed execution
pvm/ files for distributed execution over PVM
envir/ common code for user interfaces
cmdenv/ command-line user interface
tkenv/ Tcl/Tk-based user interface
gned/ graphical NED editor
plove/ output vector analyzer and plotting tool
utils/ makefile-autocreator etc
Sample simulations are within the samples directory. Each of the sample directories contain a network description (.ned file) and corresponding simple module code (.h, .cc files). Makefiles are included. The contrib directory contains material from the OMNeT++ community.
omnetpp/
samples/ directories for sample simulations
nim/ a simple two-player game
hcube/ hypercube network with deflection routing
token/ Token-Ring network
fddi/ an accurate FDDI MAC simulation
hist/ demo of the histogram classes
dyna/ dynamic module creation (client-server network)
pvmex/ demonstrates distributed execution
fifo1/ single-server queue
fifo2/ another implementation of a single-server queue
demo/ several sim. models in a single executable (NEW)
contrib/ directory for contributed material
octave/ Octave scripts for result processing
emacs/ NED syntax highlight for Emacs
This chapter contains a full example program that can give you some basic idea of using the simulator. An enhanced version of the NIM example can be found among the sample programs.
Nim is an ancient game with two players and a bunch of sticks. The players take turns, removing 1, 2, 3 or 4 sticks from the heap of sticks at each turn. The one who takes the last stick is the loser.
Of course, building a model of the Nim game is not much of a simulation project, but it nicely demonstrates the modeling approach used by OMNeT++.
Describing the model consists of two phases:
The game can be modelled in OMNeT++ as a network with three modules: the "game" (a manager module) and two players. The modules will communicate by exchanging messages. The "game" module keeps the current number of tokens and organizes the game; in each turn, the player modules receives the number of tokens from the Game module and sends back its move.
Player1, Player2 and Game are simple modules (e.g. they have no submodules.) Each module is an instance of a module type. We'll need a module type to represent the Game module; let's call it Game too.
We can implement two kinds of players: SmartPlayer, which knows the winning algorithm, and SimplePlayer, which simply takes a random number of sticks. In our example, Player1 will be a SmartPlayer and Player2 will be a SimplePlayer .
The enclosing module, Nim is a compound module (it has submodules). It is also defined as a module type of which one instance is created, the system module.
Modules have input and output gates (the tiny boxes labeled in, out, from_player1, etc. in the figure). An input and an output gate can be connected: connections (or links) are shown as in the figure as arrows. During the simulation, modules communicate by sending messages through the connections.
The user defines the topology of the network in NED files.
We placed the model description in two files; the first file defines the simple module types and the second one the system module.
The first file (NED keywords are typed in boldface):
//---------------------------------------------------------
// file: nim_mod.ned
// Simple modules in nim.ned
//---------------------------------------------------------
// Declaration of simple module type Game.
simple Game
parameters:
num_sticks, // initial number of sticks
first_move; // 1=Player1, 2=Player2
gates:
in: from_player1, // input and output gates
from_player2; // for connecting to Player1/Player2
out: to_player1,
to_player2;
endsimple
// Now the declarations of the two simple module types.
// Any one of the two types can be Player1 or Player2.
// A player that makes random moves
simple SimplePlayer
gates:
in: in; // gates for connecting to Game
out: out;
endsimple
// A player who knows the winning algorithm
simple SmartPlayer
gates:
in: in; // gates for connecting to Game
out: out;
endsimple
The second file:
//-------------------------------------------------------------
// file: nim.ned
// Nim compound module + system module
//-------------------------------------------------------------
import "nim_mod";
module Nim
submodules:
game: Game
parameters:
num_sticks = intuniform(21, 31),
first_move = intuniform(1, 2);
player1: SmartPlayer;
player2: SimplePlayer;
connections:
player1.out --> game.from_player1,
player1.in <-- game.to_player1,
player2.out --> game.from_player2,
player2.in <-- game.to_player2;
endmodule
// system module creation
network
nim: Nim
endnetwork
The module types SmartPlayer, SimplePlayer and Game are implemented in C++, using the OMNeT++ library classes and functions.
Each simple module type is derived from the C++ class cSimpleModule, with its activity() member function redefined. The activity() functions of all simple modules in the network are executed as coroutines, so they appear as if they were running in parallel. Messages are instances of the class cMessage.
We present here the C++ sources of the SmartPlayer and Game module types.
The SmartPlayer first introduces himself by sending its name to the Game module. Then it enters an infinite loop; with each iteration, it receives a message from Game with the number of sticks left, it calculates its move and sends back a message containing the move.
Here's the source:
#include <stdio.h>
#include <string.h>
#include <time.h>
#include "omnetpp.h"
// derive SmartPlayer from cSimpleModule
class SmartPlayer : public cSimpleModule
{
Module_Class_Members( SmartPlayer, cSimpleModule, 8192)
// this is a macro; it expands to constructor definition etc.
// 8192 is the size for the coroutine stack (in bytes)
virtual void activity();
// this redefined virtual function holds the algorithm
};
// register the simple module class to OMNeT++
Define_Module( SmartPlayer );
// define operations of SmartPlayer
void SmartPlayer::activity()
{
int move;
// initialization phase: send module type to Game module
// create a message with the name "SmartPlayer" and send it to Game
cMessage *msg = new cMessage("SmartPlayer");
send(msg, "out");
// infinite loop to process moves;
// simulation will be terminated by Game
for (;;)
{
// messages have several fields; here, we'll use the message
// kind member to store the number of sticks
cMessage *msgin = receive(); // receive message from Game
int num_sticks = msgin->kind();// extract message kind (an int)
// this hold the number of sticks
// still on the stack
delete msgin; // dispose of the message
move = (num_sticks + 4) ; // calculate move
if (move == 0) // we cannot take zero
move = 1; // seems like we going to lose
ev << "Taking " << move // some debug output. The ev
<< " out of " << num_sticks // object represents the user
<< " sticks.\n"; // interface of the simulator
cMessage *msgout = new cMessage; // create empty message
msgout->setKind( move ); // use message kind as storage
// for move
send( msgout, "out"); // send the message to Game
}
}
The Game module first waits for a message from both players and extracts the message names that are also the players' names. Then it enters a loop, with the player_to_move variable alternating between 1 and 2. With each iteration, it sends out a message with the current number of sticks to the corresponding player and gets back the number of sticks taken by that player. When the sticks are out, the module announces the winner and ends the simulation.
The source:
//-------------------------------------------------------------
// file: game.cc
// (part of NIM - an OMNeT++ demo simulation)
//-------------------------------------------------------------
#include <stdio.h>
#include <string.h>
#include "omnetpp.h"
// derive Game from cSimpleModule
class Game : public cSimpleModule
{
Module_Class_Members(Game,cSimpleModule,8192)
// this is a macro; it expands to constructor definition etc.
// 8192 is the size for the coroutine stack (in bytes)
virtual void activity();
// this redefined virtual function holds the algorithm
};
// register the simple module class to OMNeT++
Define_Module( Game );
// operation of Game:
void Game::activity()
{
// strings to store player names; player[0] is unused
char player[3][32];
// read parameter values
int num_sticks = par("num_sticks");
int player_to_move = par("first_move");
// waiting for players to tell their names
for (int i=0; i<2; i++)
{
cMessage *msg = receive();
if (msg->arrivedOn("from_player1"))
strcpy( player[1], msg->name());
else
strcpy( player[2], msg->name());
delete msg;
}
// ev represents the user interface of the simulator
ev << "Let the game begin!\n";
ev << "Player 1: " << player[1] << " Player 2: " << player[2]
<< "\n\n";
do
{
ev << "Sticks left: " << num_sticks << "\n";
ev << "Player " << player_to_move << " ("
<< player[player_to_move] << ") to move.\n";
cMessage *msg = new cMessage("", num_sticks);
// num_sticks will be the msg kind
if (player_to_move == 1)
send(msg, "to_player1");
else
send(msg, "to_player2");
msg = receive();
int sticks_taken = msg->kind();
delete msg;
num_sticks -= sticks_taken;
ev << "Player " << player_to_move << " ("
<< player[player_to_move] << ") took "
<< sticks_taken << " stick(s).\n";
player_to_move = 3 - player_to_move;
}
while (num_sticks>0);
ev << "\nPlayer " << player_to_move << " ("
<< player[player_to_move] << ") won!\n";
endSimulation();
}
Once the source files are ready, one needs to compile and link them into a simulation executable. One can specify the user interface to be linked.
Before running the simulation, one can put parameter values and all sorts of other settings into an initialization file that will be read when the simulation program starts:
;--------------------- ; file: omnetpp.ini ;--------------------- [General] network = nim random-seed = 3 ini-warnings = false [Cmdenv] module-messages = yes verbose-simulation = no
Suppose we link the NIM simulation with the command line user interface. We get the executable nim (nim.exe under Windows). When we run it, we'll get the following screen output:
./nim
Or:
C:\OMNETPP\SAMPLES\NIM> nim.exe OMNeT++ Discrete Simulation, TUB Dept. of Telecommunications, 1990-97 Preparing for Run #1... Setting up network `nim'... Running simulation... Let the game begin! Player 1: SmartPlayer Player 2: SimplePlayer Sticks left: 29 Player 2 (SimplePlayer) to move. SimplePlayer is taking 2 out of 29 sticks. Player 2 (SimplePlayer) took 2 stick(s). Sticks left: 27 Player 1 (SmartPlayer) to move. SmartPlayer is taking 1 out of 27 sticks. Player 1 (SmartPlayer) took 1 stick(s). Sticks left: 26 [...] Sticks left: 5 Player 1 (SmartPlayer) to move. SmartPlayer is taking 4 out of 5 sticks. Player 1 (SmartPlayer) took 4 stick(s). Sticks left: 1 Player 2 (SimplePlayer) to move. SimplePlayer is taking 1 out of 1 sticks. Player 2 (SimplePlayer) took 1 stick(s). Player 1 (SmartPlayer) won! <!> Module nim.game: Simulation stopped with endSimulation(). End run of OMNeT++
An enhanced version of the NIM example can be found among the sample programs. It adds a third, interactive player and derives specific player types from a Player abstract class. It also adds the possibility that actual types for player1 and player2 can be specified in the ini file or interactively entered by the user at the beginning of the simulation.
Nim does not show very much of how complex algorithms like communication protocols can be implemented in OMNeT++. To have an idea about that, look at the Token Ring example. It is also extensively commented, though you may need to peep into the user manual to fully understand it.
Other programs in the example manual are Dyna and FDDI. Dyna models a simple client-server network and demonstrates dynamic module creation. The FDDI example is an accurate FDDI MAC simulation which was written on the basis of the ANSI standard.
The following table summarizes the sample simulations:
| NAME | TOPIC | DEMONSTRATES |
nim | a simple two-player game | module inheritance module type as parameter |
hcube | hypercube network with deflection routing | hypercube topology with dimension as parameter topology templates output vectors |
token | Token Ring network | ring topology with the number of nodes as parameter using cQueue wait() and the putaside-queue output vectors |
fifo1 | single-server queue | simple module inheritance decomposing activity() into several functions using simple statistics and output vectors printing stack usage info to help optimize memory consumption using finish() |
fifo2 | another fifo implementation | using handleMessage()
decomposing handleMessage() into several functions the FSM macros simple module inheritance using simple statistics and output vectors using finish() |
fddi | FDDI MAC simulation | using statistics classes and many other features |
hist | demo of the histogram classes | collecting observations into statistics objects saving statistics objects to file and restoring them using the inspect.lst file in Tkenv |
dyna | a client-server network | dynamic module creation using WATCH() star topology with the number of modules as parameters |
pvmex | distributed execution | distributed execution |
demo(NEW) | tour of OMNeT++ samples | shows how to link several sim. models into one executable |
The description of model topology is given in the NED language. The NED language supports modular description of a network. This means that a network description consists of a number of component descriptions (channels, simple/compound module types). The channels, simple modules and compound modules of one network description can be used in another network description. As a consequence, the NED language makes it possible for the user to build his own libraries of network descriptions.
Files containing network descriptions generally have a .ned suffix. Network descriptions are not used directly: they are translated into C++ code by the NEDC compiler, then compiled by the C++ compiler and linked into the simulation executable.
The EBNF description of the language can be found in the appendix.
A NED description can contain the following components, in arbitrary number or order:
The rest of this chapter discusses each of these types in detail.
The writer of the network description has to take care that no reserved words are used for names. The reserved words of the NED language are:
import include channel endchannel simple endsimple module endmodule error delay datarate const parameters gates submodules connections gatesizes on if machines for do endfor network endnetwork nocheck ref ancestor true false like input numeric string bool char
The network description and all identifiers in it are case sensitive.
Example:
import "tkn_mod", "tkn2_mod";
The import statement (the include keyword is also recognized for backwards compatibility) is used to import declarations from other network description files. After importing a network description, one can use the components (channels, simple/compound module types) defined in it.
From the imported files, only the declaration information is used, but no C++ code is generated. The consequence is that one has to compile and link each network description, not only the top-level ones.
The user can specify the name of the files with or without the .ned extension. One can also include a path in the filenames, or better, use the NEDC compiler's -I <path> command-line option to name the directories where the imported files reside.
A channel definition specifies a connection type of given characteristics. The channel name can be used later in the NED description to create connections with these parameters.
Example:
channel DialUpConnection delay normal (0.004, 0.0018) error 0.00001 datarate 14400 endchannel
Any of the delay, error and datarate parameters are optional and they can appear in any order. The values are NED expressions. This means that they can be constants (integer or real), random values from various distributions, etc.
Simple modules are the basic building blocks for other (compound) modules. A simple module is defined by declaring its parameters and gates.
Example:
simple SomeNameForModule parameters: //... gates: //... endsimple
Parameters are variables that belong to a module. Simple module parameters can be queried and used by simple module algorithms. For example, a parameter called num_of_messages can be used by a module called MsgSource to determine how many messages it has to generate.
Parameters are declared by listing their names in the parameters: section of a module description. The parameter type can optionally be specified as numeric, numeric const (or simply const), bool, string, or anytype:
Example:
simple MsgSource
parameters:
interarrival_time,
num_of_messages : const,
address : string;
gates: //...
endsimple
If the parameter type is omitted, numeric is assumed. Practically, this means that you only need to explicitly specify the type for string, bool or char-valued parameters.
Note that the actual parameter values are given later, when the module is used as a building block of a compound module type or as a system module.
When the user writes the word const before the parameter, it is converted to constant; that is, the parameter's value is replaced by its evaluation. This can be important when the original value was a random number or an expression. One is advised to write out the const keyword for each parameter that should be constant.
Beware when using const and by-reference parameter passing (ref modifier, see later) at the same time. Converting the parameter to constant can affect other modules and cause errors that are difficult to discover.
Gates are the connection points of modules. The starting and ending points of the connections between modules are gates. OMNeT++ supports simplex (one-directional) connections, so there are two kinds of gates: input and output. Messages are sent through output gates and received through input gates.
Gates are identified with their names. Gate vectors are supported: a gate vector contains a number of single gates.
Gates are declared by listing their names in the gates: section of a module description. An empty bracket pair [] denotes a gate vector. Elements of the vector are numbered from zero.
Examples:
simple DataLink parameters: //.. gates: in: from_port, from_higher_layer; out: to_port, to_higher_layer; endsimple simple RoutingModule parameters: //... gates: in: output[]; out: input[]; endsimple
The sizes of gate vectors are given later, when the module is used as a building block of a compound module type. Thus, every instance of the module can have gate vectors of different sizes.
Compound modules are modules that are composed of one or more submodules. Compound modules, like a simple modules, can have parameters and gates, so a compound module definition looks similar to a simple module definition, except that it also has sections to specify the submodules and connections within the module.
Submodules can either be simple or compound modules, they are equivalent.
Example:
module SomeNameForCompoundModule parameters: //... gates: //... submodules: //... connections: //... endmodule
Any of the above sections (parameters, gates, submodules, connections) is optional.
Parameters are declared in the same way as with simple modules. Please refer to the "Simple module parameters" section.
Example:
module Router parameters: rte_processing_delay, rte_buffersize, num_of_ports : const; gates: //... submodules: //... connections://... endmodule
Compound module parameters can be used in two ways:
For example, a parameter called num_of_ports can be used to construct a router module with the number of ports as a parameter.
Gates have the same role and are declared in the same way as with simple modules. Please refer to the "Simple module gates" section.
Example:
module Router parameters: //... gates: in: input_port[]; out: output_port[]; submodules: //... connections: //... endmodule
Submodules are defined in the submodules: section of a module description. For each submodule, there are sections to define the actual values to be passed to its parameters and the sizes of its gate vectors.
Example:
module NameForCompoundModule parameters: //... gates: //... submodules: SubModuleName: TypeOfSubModule parameters: //... gatesizes: //... SecondSubModuleName: TypeOfSecondSubModule //... connections: //... endmodule
In a submodule definition, one has to supply the name of a previously defined module as the type and a module name. The description of the module type can occur in the same network description or in an imported network description.
Module vector as submodule
It is possible to create an array of submodules (a module vector). This is done with an expression between brackets right behind the module type name. The expression can refer to module parameters. A zero value as module count is also allowed.
Example:
module BigCompound parameters: num_of_submods: const; submodules: Submod1: Node[3] //... Submod2: Node[num_of_submods] //... Submod3: Node[(num_of_submods+1)/2] //... endmodule
Module type as parameter
Instead of supplying a concrete module type, one can leave it as a parameter. At the same time, to let the NED compiler know what parameters and gates that module has, the user has to supply the name of an existing module type. This is done with the like phrase.
Example:
module CompoundModule
parameters:
node_type : string;
gates: //...
submodules:
theNode: node_type like GeneralNode
parameters:
buffer = 10;
connections: //...
endmodule
The above example means that the type of the submodule theNode is not known in advance; it will be taken from the node_type parameter of CompoundModule which must be a string (for example, "SwitchingNode"). The module type called GeneralNode must have appeared earlier in the NED files; its declaration will be used to check whether theNode's parameters and gates exist and are used correctly. The node_type parameter will probably be given an input value somewhere higher in the module hierarchy so that the actual module type can be specified in the ini file or entered interactively.
The GeneralNode module type does not need to be implemented in C++, because no instance of it is created; it is merely used to check the correctness of the NED file.
On the other hand, the actual module type that will be substituted (i.e. SwitchingNode in our case) does not need to be declared in the NED files.
The like phrase enables the user to create families of modules that serve similar purposes and implement the same interface (they have the same gates and parameters) and to use them interchangeably in NED files. This scheme directly parallels with the concept of polymorphism used in object-oriented programming.
Submodule parameters
Right after the declaration, the values for the parameters of the declared submodules can be specified.
Example:
module ManyParameters parameters: par1, par2, switch; submodules: Submod1: Node parameters: p1 = 10, p2 = par1+par2, p3 = switch==0 ? par1 : par2; //... endmodule
Expressions are mostly C-style, and they can contain parameters of the compound module being defined. A separate section is dedicated to expressions. Here, only the modes of parameter passing are discussed.
The default parameter passing method is by value. However, the user can write ref or ancestor before the parameter name. Writing ref means that the parameter is not passed by value, but by reference. This means that instead of the value of the parameter the address of the parameter is passed.
Writing ancestor before the parameter name means that the parameter will be searched upwards, among the parameters of all future enclosing modules of the current module. This reference cannot be resolved or checked by the NEDC compiler; it can only be done at runtime, when the whole network has been built up. The parameter which is found first is used; if no such parameter can be found in any of the enclosing modules, the system will give an error during runtime.
The ancestor and ref modifiers are independent, they can be used together.
For example:
simple sub_sub parameters: s_s_par1, s_s_par2; endmodule sub_sub
module sub parameters: s_par; submodules: child: sub_sub parameters: s_s_par1 = ref s_par, s_s_par2 = ref ancestor m_par2; endmodule sub module mod parameters: m_par1, m_par2; submodules: child: sub parameters: s_par = m_par1; endmodule mod
Again, note that the network description compiler can check for the existence of ordinary parameters but not for ancestor parameters (it cannot predict in what modules the current module will be embedded in an actual network description).
Parameters taken by reference can be used as a second means of module communication, because during simulation execution, if a module changes the value of a parameter taken be reference, the changed value propagates to other modules. ref parameters can also be used to implement shared memory (see in Chapter 5).
Submodule gate sizes
The sizes of gate vectors are defined with the gatesizes: keyword. Gate vector sizes can be given as constants, parameters or expressions.
An example:
simple SimpleType gates: in: inputs[]; out: outputs[]; endsimple module SomeCompound parameters: num: const; submodules: Submod1: SimpleType gatesizes: inputs[10], outputs[num]; //... endmodule
Conditional parameter and gatesize sections
Multiple parameters: and gatesizes: sections can exist in a submodule definition and each of them can be tagged with conditions.
For example:
module Serial:
parameters: count: const;
submodules:
node : Node [count]
parameters:
position = "middle";
parameters if index==0:
position = "beginning";
parameters if index==count-1:
position = "end";
gatesizes:
in[2], out[2];
gatesizes if index==0 || index==count-1:
in[1], in[1];
connections:
//...
endmodule
If the conditions are not disjunct and a parameter value or a gate size is defined twice, the last definition will take effect, overwriting the former ones. Thus, values intended as defaults should appear in the first sections.
In a compound module definition, the gates of the compound module and its immediate submodules are connected. In other words, the NED language does not support connections that would cross "the walls" of a compound module without using gates of that module. Only point-to-point connections are supported.
In summary:
Connections are specified in the Connections: section of a compound module definition. It lists the connections, separated with semicolons.
Example:
module SomeCompound: parameters: //... gates: //... submodules: //... connections: node1.output --> node2.input; node1.input <-- node2.output; //... endmodule
Each connection can be:
These connection types are described in the following sections.
Single connections and channels
The source gate can be an output gate of a submodule or an input gate of the compound module, and the destination gate can be an input gate of a submodule or an output gate of the compound module.
If the user does not specify a channel, the connection will have no propagation delay, no transmission delay and no bit errors:
Sender.outgate --> Receiver.ingate;
The arrow can point either left-to-right or right-to-left.
The user can also specify a channel by its name:
Sender.outgate --> Dialup14400 --> Receiver.ingate;
In this case, the NED sources must contain the definition of the channel.
One can also specify the channel parameters directly:
Sender.outgate --> error 1e-5 delay 0.001 --> Receiver.ingate;
Either of the parameters can be omitted and they can be in any order.
Loop connections
If submodule or gate vectors are used, it is possible to create more than one connection with one statement. This is termed a multiple or loop connection.
A multiple connection is created with the for statement:
for i=0..4 do Sender.outgate[i] --> Receiver[i].ingate endfor
The result of the above loop connection can be illustrated as follows:
One can place several connections in the body of the for statement, separated with semicolons.
More than one indices can be specified in a for statement, with their own lower and upper bounds. This will be interpreted as nested for statements, the leftmost index being the outermost and the rightmost index being the innermost loop.
for i=0..4, j=0..4 do //... endfor
One can also use an index in the lower and upper bound expressions of the subsequent indices:
for i=0..3, j=i+1..4 do //... endfor
In the above example, the following (i,j) pairs will be used for the connections inside the for statement:
(0,1) (0,2) (0,3) (0,4) (1,2) (1,3) (1,4) (2,3) (2,4) (3,4)
A gate cannot be used in more than one connection and one connection cannot be made more than once. Consider the following bogus statement:
for i = 0..2, j = 0..2 do module1.out [i] --> module2.in [i]; endfor
It will cause a runtime error: each connection is made twice, as the index variable j is not used in the connection. In general, every connection inside a loop should use all the index variables at both sides of the connection.
Conditional connections
Connections can be conditional. This is a conditional connection:
for i=0..n do Sender.outgate[i] --> Receiver[i].ingate if i==0; endfor
This way we connected every second gate.
The nocheck modifier
Conditional connections are especially useful with random numbers when they can create random connections. Here, a problem can be that by default, the simulation program checks if all gates are connected. You can turn off this check by using the nocheck modifier.
This example generates a random subgraph of a full graph:
module Stochastic: parameters: //.. gates: //.. submodules: //.. connections nocheck: for i=0..9 do Sender.outgate[i] --> Receiver[i].ingate if uniform(0,1)<0.3; endfor endmodule
When using nocheck, it is the simple modules' responsibility not to send messages on gates that are not connected.
With the help of conditional parameter and gatesize blocks and conditional connections, one can create complex topologies.
Example 1: Router
The following example contains a router module with the number of ports taken as parameter. The compound module is built using three module types: Application, RoutingModule, DataLink. We assume that their definition is in a separate NED file which we will import.
import "modules"; module Router: parameters: rte_processing_delay, rte_buffersize, num_of_ports: const; gates: in: input_ports[]; out: output_ports[]; submodules: local_user: Application; routing: RoutingModule parameters: processing_delay = rte_processing_delay, buffersize = rte_buffersize; gatesizes: input[num_of_ports+1], output[num_of_ports+1]; port_if: DataLink[num_of_ports] parameters: retry_count = 5, window_size = 2; connections: for i=0..num_of_ports-1 do routing.output[i] --> port_if[i].from_higher_layer; routing.input[i] <-- port_if[i].to_higher_layer; port_if[i].to_port --> output_ports[i]; port_if[i].from_port <-- input_ports[i]; endfor; routing.output[num_of_ports] --> local_user.input; routing.input[num_of_ports] <-- local_user.output; endmodule
Example 2: Chain
For example, one can create a chain of modules like this:
module Serial: parameters: count: const; submodules: node : Node [count] gatesizes: in[2], out[2]; gatesizes if index==0 || index==count-1: in[1], out[1]; connections: for i = 0..count-2 do node[i].out[i!=0 ? 1 : 0] --> node[i+1].in[0]; node[i].in[i!=0 ? 1 : 0] <-- node[i+1].out[0]; endfor endmodule
Example 3: Binary Tree
Building a binary tree is a good example of using conditional connections:
simple BinaryTreeNode:
gates:
in: from_up, from_downleft, from_downright;
out: upward, downleft, downright;
endsimple
module BinaryTree:
parameters: height: const;
submodules: node: BinaryTreeNode [ 2^height-1 ];
//....
connections:
for i = 0..2^height-2, j = 0..2^height-2 do
node[i].upward --> node[j].from_downleft if leftchild(i,j);
node[i].from_up <-- node[j].downleft if leftchild(i,j);
node[i].upward --> node[j].from_downright if rightchild(i,j);
node[i].from_up <-- node[j].downright if rightchild(i,j);
endfor
//....
endmodule
The dotted lines should be replaced by modules that close the tree at its root and the lower edge. The leftchild(i,j) and rightchild(i,j) functions are:
leftchild(i,j) =
rightchild(i,j) =
These formulas can be directly substituted in the NED description, or alternatively, written in C and linked into the simulation executable.
Example 4: Random graph
Conditional connections can also be used to generate random topologies. The following code generates a random subgraph of a full graph:
module RandomGraph: parameters: count: const, connectedness; // 0.0<x<1.0 submodules: node: Node [count] gatesizes: in[count], out[count]; connections nocheck: for i=0..count-1, j=0..count-1 do node[i].out[j] --> node[j].in[i] if i!=j and uniform(0,1)<connectedness; endfor endmodule
Note that not each gate of the modules will be connected. By default, an unconnected gate produces a run-time error message when the simulation is started, but this error message is turned off here with the nocheck modifier. Consequently, it is the simple modules' responsibility not to send on a gate which is not leading anywhere.
Since parameter values can be used in defining the internal topology of the module, the const modifier has a significant role. Consider the following example:
simple Sender parameters: num_of_outgates; gates: out: outgate[num_of_outgates]; endsimple Sender simple Receiver gates: in: ingate; endsimple Receiver module Network; parameters: num_of_mods: const; submodules: sender: Sender parameters: num_of_outgates = num_of_mods; receiver: Receiver [num_of_mods] connections: for i=1..num_of_mods do sender.outgate[i] --> receiver[i].ingate endfor; endmodule network net: Network parameters: num_of_mods = normal (5,2); endnetwork
If parameter num_of_mods wasn't const, the following would happen:
normal(5,2) would be substituted for the num_of_mods. There are three places where an evaluation of num_of_mods (that is, normal (5,2)) is done (they are typed in italics in the example). It is likely that these evaluations would not result in the same value, and consequently, the gate vector sizes would not match each other and the end value of the for statement. Thus, the loop connection would not be created properly.
Using const for the parameter num_of_mods prevents this from happening: an evaluation of normal(5,2) is substituted for num_of_mods and an equal number of gates are created.
Several approaches can be used when you want to create complex topologies which have a regular structure; three of them are described below.
'Subgraph of a Full Graph'
This pattern takes a subset of the connections of a full graph. A condition is used to "carve out" the necessary interconnection from the full graph:
for i=0..N-1, j=0..N-1 do node[i].out[...] --> node[j].in[...] if condition(i,j); endfor
The RandomGraph compound module (presented earlier) is an example of this pattern, but the pattern can generate any graph where an appropriate condition(i,j) can be formulated. For example, when generating a tree structure, the condition would return whether node j is a child of node i or vica versa.
Though this pattern is very general, its usage can be prohibitive if the N number of nodes is high and the graph is sparse (it has much fewer connections that N2). The following two patterns do not suffer from this drawback.
'Connections of Each Node'
The pattern loops through all nodes and creates the necessary connections for each one. It can be generalized like this:
for i=0..Nnodes, j=0..Nconns(i)-1 do node[i].out[j] --> node[rightNodeIndex(i,j)].in[j]; endfor
The Hypercube compound module (to be presented later) is a clear example of this approach. BinaryTree can also be regarded as an example of this pattern where the inner j loop is unrolled.
The applicability of this pattern depends on how easily the rightNodeIndex(i,j) function can be formulated.
'Enumerate All Connections'
A third pattern is to list all connections within a loop:
for i=0..Nconnections-1 do node[leftNodeIndex(i)].out[...] --> node[rightNodeIndex(i)].in[...]; endfor
The pattern can be used if leftNodeIndex(i) and rightNodeIndex(i) mapping functions can be sufficiently formulated.
The Serial module is an example of this approach where the mapping functions are extremely simple: leftNodeIndex(i)=i and rightNodeIndex(i)=i+1. The pattern can also be used to create a random subset of a full graph with a fixed number of connections.
In the case of irregular structures where none of the above patterns can be employed, the user can resort to specifying constant submodule/gate vector sizes and explicitly listing all connections, like he/she would do it in most existing simulators.
Overview
Topology templates are nothing more than compound modules where one or more submodule types are left as parameters (using the like phrase of the NED language). You can write such modules which implement mesh, hypercube, butterfly, perfect shuffle or other topologies, and you can use them wherever needed in you simulations. With topology templates, you can reuse interconnection structure.
An example: hypercube
The concept is demonstrated on a network with hypercube interconnection. When building an N-dimension hypercube, we can exploit the fact that each node is connected to N others which differ from it only in one bit of the binary representations of the node indices (see figure).
The hypercube topology template is the following (it can be placed into a separate file, e.g hypercube.ned):
simple Node
gates: out: out[]; in: in[];
endsimple
module Hypercube
parameters:
dim, nodetype;
submodules:
node: nodetype[2^dim] like Node
gatesizes:
out[dim], in[dim];
connections:
for i=0..2^dim-1, j=0..dim-1 do
node[i].out[j] --> node[i # 2^j].in[j]; // # is bitwise XOR
endfor
endmodule
When you create an actual hypercube, you substitute the name of an existing module type (e.g. Hypercube_PE) for the nodetype parameter. The module type implements the algorithm the user wants to simulate and it must have the same gates that the Node type has. The topology template code can be used through importing the file:
import "hypercube.ned"
simple Hypercube_PE
gates: out: out[]; in: in[];
endsimple
network hypercube: Hypercube
parameters:
dim = 4,
nodetype = "Hypercube_PE";
endnetwork
If you put the nodetype parameter to the ini file, you can use the same simulation model to test e.g. several routing algorithms in a hypercube, each algorithm implemented with a different simple module type - you just have to supply different values to nodetype, such as "WormholeRoutingNode", "DeflectionRoutingNode", etc.
A network definition (or system module definition) specifies the system module. In its syntax, it is very similar to a submodule declaration. The system definition starts with keyword network and ends with endnetwork.
An example:
network modelledNetwork: SomeModule parameters: par1=10, par2=normal(100,20); endnetwork
Here, SomeModule is the name of a compound or a simple module type.
There can be several system definitions in a network description, each one defines a different network. The simulation program built with such a network description is able to run any of them; the desired one can be specified in the config file (see later).
OMNeT++ simulations can be executed in parallel. This means that different parts of the model execute on different hosts or processors. (We'll use the term "host" or "machine" in this sense.) The unit of granularity is the simple module: one simple module always executes on a single processor.
Parallel execution is also supported by NED: the language provides an elegant way of specifying execution hosts for different modules. We'll discuss this feature in the following sections.
To support the segmentation of the model for execution of different modules, the compound module definition was extended with the machines: and the on: keywords.
Example:
module SomeNameForCompoundModule machines: host1, host2, host3, host4; parameters: //... gates: //... submodules: submodule1 : submodtype1 on: host1; submodule2 : submodtype2 on: host2, host3; submodule3 : submodtype1 on: host4; connections: //... endmodule
The machines: section lists formal host names which are used in the on: lists of the submodules.
In the example, the second submodule is itself a compound module that can be further subdivided to run on two separate hosts, so its definition must have a machines: section with two parameters. You do not have to propagate host names down to simple module level: you can stop at a compound module which executes on a single host. In other words, a compound module with no machines: section is equivalent to one with one machine parameter.
Of course, you can give the same value to several machine parameters, as to submodule1's in the following example. In this case, the whole compound module will be placed on a single host, as if it never had machine parameters at all.
module AnotherCompoundModule machines: host1, host2; parameters: //... gates: //... submodules: submodule1 : submodtype1 on: host1, host1, host1; //... connections: //... endmodule
Host names propagate up to network definition level. Extension to the network definition:
network distVector: DistVector on: machine1, machine2, machine3; endnetwork
The on: parameters of the network definition can be actual host names, or alternatively, they can be symbolic names that are mapped to actual host names in the config file.
Similarly to the parameters: and gatesizes: section, multiple on: sections can exist for the submodules if they are tagged with if phrases.
This makes it possible to control the module distribution with parameters. You can even put different parts of a module vector on different machines using the index operator (see later in the section describing expressions).
Example:
module DistVector: machines: host1, host2, host3; submodules: node : Node [count] on if index<count*.33: host1; on if index>=count*.33 && index<count*.66: host2; on if index>=count*.66: host3; endmodule network distvector: DistVector on machine1, machine2, machine3; endnetwork
In the NED language there are a number of places where expressions are expected.
When such an expression is encountered by the NEDC compiler, it is compiled and it will be evaluated run-time.
Expressions have a C-style syntax. They are built with the usual math operators; they can use parameters taken by value or by reference; call C functions; contain random and input values etc.
Expressions can use the parameters of the compound module being built. A parameter can be taken by value or by reference. The default is by value; to select by-reference passing for a parameter, you have to use the ref modifier. Parameters passed by reference can be used by a module to propagate values (status info etc.) to other modules.
The ancestor modifier allows one to access parameters from higher in the module hierarchy.
module Compound parameter: nnn; submodules: proc: Processor parameters: par1 = ref nnn / 2, par2 = 10 * ancestor par_somewhere_up; endmodule
The following operators can be used in expressions, in order of precedence:
operator | meaning |
-, !, ~ | unary minus, negation, bitwise complement |
^ | power of |
*, /, | multiply, divide, modulus |
+, - |
add, subtract |
<<, >> | bitwise shifting |
&, |, # | bitwise and, or, xor (^ is reserved for power) |
== |
equal |
!= |
not equal |
>, >= | greater, greater or equal |
<, <= | less, less or equal |
&&, ||, ## | logical operators and, or, xor |
?: |
the C/C++ "inline if" |
A useful operator is sizeof(), which gives the size of a vector gate. The index operator gives the index of the current submodule in its module vector.
An example for both:
module Compound gates: in: fromgens[]; submodules: proc: Processor[ sizeof(fromgens) ]; parameters: address = 10*(1+index); connections: for i = 0 .. sizeof(fromgens)-1 do in[i] --> proc[i].input; endfor endmodule
Here, we create as many processors as there are input gates for this compound module in the network. The address parameters of the processors are 10, 20, 30 etc.
Anywhere you would put numeric constants (integer or real) to mean time in seconds, you can also specify the time in units like milliseconds, minutes or hours:
...
parameters: propagation_delay = 560ms, // 0.560s
connection_timeout = 6m 30s 500ms, // 390.5s
lunchtime = 0.5h; // 30 min
The following units can be used:
| nanoseconds | *10-9 | |
| microseconds | *10-6 | |
| milliseconds | *10-3 | |
| seconds | *1 | |
| minutes | *60 | |
| hours | *3600 | |
| days | *60*3600 |
OMNeT++ has the following predefined distributions:
Each distribution has one or more parameters.
Examples:
uniform(0,1) // uniform in [0,1) intuniform(-2,2) // uniform int, limits included: -2,-1,0,1,or 2 exponential(5) // exponential with mean=5 (thus parameter=0.2) normal(100,5) // mean 100, variance 5 truncnormal(5,3) // normal distr, truncated to nonnegative values
The functions all use the random number generator 0. By using the genk_-prefixed versions of the above functions, you can specify which generator should be used. The index of the generator comes as the first argument.
Example:
genk_normal(2,100,5) // as normal(100,5), using generator 2
The above distributions are implemented with C functions (see later in the Functions section). This also means that you can easily add further ones by writing their code in C++ and using the Register_Function macro. Your distributions will be treated in the same way as the built-in ones.
The syntax is:
input( 10, "Number of processors:" )
Or you can omit the prompt text:
input( 10ms )
Value for input parameters can be given in the config file. If they are not there, the user will be offered a prompt to enter the value.
In NED expressions, you can use mathematical functions:
To use user-defined functions, one has to code the function in C++. The C++ function must take 0, 1, 2, or 3 arguments of type double and return a double. The function must be registered in one of the C++ files with the Define_Function() macro.
An example function (the following code must appear in one of the C++ sources):
double average(double a, double b)
{
return (a+b)/2;
}
Define_Function(average, 2)
The number 2 means that the average() function has 2 arguments. After this, the average() function can be used in NED files:
module Compound parameter: a,b; submodules: proc: Processor parameters: av = average(a,b); endmodule
An important application of this concept is to extend OMNeT++ with new distributions.
Display strings specify the arrangement and appearance of modules in graphical user interfaces (currently only Tkenv): they control how the objects (compound modules, their submodules and connections) are displayed. Display strings occur in NED description's display: phrases.
The display string format is a semicolon-separated list of tags. Each tag consists of a key (usually one letter), an equal sign and a comma-separated list of parameters, like:
"p=100,100;b=60,10,rect;o=blue,black,2"
Parameters may be omitted also at the end and also inside the parameter list, like:
"p=100,100;b=rect;o=,,2"
Module/submodule parameters can be included with the $name notation:
"p=$xpos,$ypos;b=rect,60,10;o=$fillcolor,black,2"
Objects that may have display strings are:
Tags used in submodule display strings:
| Tag | Meaning |
p=xpos,ypos | Place submodule at (xpos,ypos) pixel position, with the origin being the top-left corner of the enclosing module.
Defaults: an appropriate automatic layout is where submodules do not overlap. If applied to a submodule vector, ring or row layout is selected automatically. |
p=xpos,ypos,row,deltax | Used for module vectors. Arranges submodules in a row starting at (xpos,ypos), keeping deltax distances.
Defaults: deltax is chosen so that submodules do not overlap. |
p=xpos,ypos,col,deltay | Used for module vectors. Arranges submodules in a column starting at (xpos,ypos), keeping deltay distances.
Defaults: deltay is chosen so that submodules do not overlap. |
p=xpos,ypos,matrix, itemsperrow,deltax,deltay | Used for module vectors. Arranges submodules in a matrix starting at (xpos,ypos), at most itemsperrow submodules in a row, keeping deltax and deltay distances.
Defaults: itemsperrow=5, deltax,deltay are chosen so that submodules do not overlap. |
p=xpos,ypos,ring, width,height | Used for module vectors. Arranges submodules in an ellipse, with the top-left corner of its bounding boxes at (xpos,ypos), with the width and height.
Defaults: width=40, height=24 |
b=width,height,rect | Rectangle with the given height and width.
Defaults: width=40, height=24 |
b=width,height,oval | Ellipse with the given height and width.
Defaults: width=40, height=24 |
o=fillcolor,outlinecolor, borderwidth | Specifies options for the rectangle or oval. Any valid Tk color specification is accepted: English color names or #rgb, #rrggbb format (where r,g,b are hex digits).
Defaults: fillcolor=#8080ff (a lightblue), outlinecolor=black, borderwidth=2 |
i=iconname | Use the named icon. No default. If no icon name is present, box is used. |
Examples:
"p=100,60;i=workstation" "p=100,60;b=30,30,rect;o=,,4"
Tags used in enclosing module display strings:
| Tag | Meaning |
p=xpos,ypos | Place enclosing module at (xpos,ypos) pixel position, with (0,0) being the top-left corner of the window. |
b=width,height,rect | Display enclosing module as a rectangle with the given height and width.
Defaults: width, height are chosen automatically |
b=width,height,oval | Display enclosing module as an ellipse with the given height and width.
Defaults: width, height are chosen automatically |
o=fillcolor,outlinecolor, borderwidth | Specifies options for the rectangle or oval. Any valid Tk color specification is accepted: English color names or #rgb, #rrggbb format (where r,g,b are hex digits).
Defaults: fillcolor=#8080ff (a lightblue), outlinecolor=black, borderwidth=2 |
Tags used in connection display strings:
| Tag | Meaning |
m=auto m=north m=west m=east m=south | Drawing mode. Specifies the exact placement of the connection arrow. The arguments can be abbreviated as a,e,w,n,s. |
m=manual,srcpx,srcpy, destpx,destpy | The manual mode takes four parameters that explicitly specify anchoring of the ends of the arrow: srcpx, srcpy, destpx, destpy. Each value is a percentage of the width/height of the source/destination module's enclosing rectangle, with the upper-left corner being the origin. Thus,
m=m,50,50,50,50 would connect the centers of the two module rectangles. |
o=color,width | Specifies the appearance of the arrow. Any valid Tk color specification is accepted: English color names or #rgb, #rrggbb specification (where r,g,b are hex digits). Defaults: color=black, width=2 |
Examples:
"m=a;o=blue,3"
The GNED editor allows you to design compound modules graphically. GNED works with NED files - it doesn't use any nasty internal file format. You can load any of your existing NED files, edit the compound modules in it graphically and then save the file back. The rest of the stuff in the NED file (simple modules, channels, networks etc.) will survive the operation. GNED puts all graphics-related data into display strings.
GNED works by parsing your NED file into an internal data structure, and regenerating the NED text when you save the file. One consequence of this is that indentation will be "canonized" -- hopefully you consider this fact as a plus and not as a minus. Comments in the original NED are preserved -- the parser associates them with the NED elements they belong to, so comments won't be messed up even if you edit the graphical representation to death by removing/adding submodules, gates, parameters, connections, etc.
GNED is now a fully two-way visual tool. While editing the graphics, you can always switch to NED source view, edit in there and switch back to graphics. Your changes in the NED source will be immediately backparsed to graphics; in fact, the graphics will be totally reconstructed from the NED source and the display strings in it.
GNED is still under development. There are some missing functions and bugs, but overall it should be fairly reliable. See the TODO file in the GNED source directory for problems and missing features.
Comment parsing:
It is useful to know how exactly GNED identifies the comments in the NED file. The following (maybe a bit long) NED code should explain it:
// ---------------------------------------------------------------
// File: sample.ned
//
// This is a file comment. File comments reach from the top of
// the file till the last blank line above the first code line.
// ---------------------------------------------------------------
//
// The file comment can also contain blank lines, so this is
// still part of the above file comment.
//
// Module1 --
//
// This is a banner comment for the Module1 declaration below.
// Banner comments can be multi-line, but they are not supposed
// to contain blank lines. (Otherwise the lines above the blank
// one will be taken as part of a file comment or trailing comment.)
//
module Module1
submodules: // and this is right-comment
// This is another banner comment, for the submodule
submod1: Module;
display: "p=120,108;b=96,72,rect";
connections:
out --> submod1.in; // Right-comments can also be
// multi-line.
endmodule
// Finally, this is a trailing comment, belonging to the above
// module. It may contain blank lines. Trailing comments are
// mostly used to put separator lines into the file, like this:
// --------------------------------------------------------------
// Module2 --
//
// an empty module
//
module Module2
endmodule
Key/mouse bindings:
In graphics view, there are two editing modes: draw and select/mode. The mouse bindings are the following:
| Mouse | Effect |
| In draw mode: | |
| Drag out a rectangle in empty area: | create new submodule |
| Drag from one submodule to another: | create new connection |
| Click in empty area: | switch to select/move mode |
| In select/move mode: | |
| Click submodule/connection: | select it |
| Ctrl-click submodule/conn.: | add to selection |
| Click in empty area: | clear selection |
| Drag a selected object: | move selected objects |
| Drag submodule or connection: | move it |
| Drag either end of connection: | move that end |
| Drag corner of (sub)module: | resize module |
| Drag starting in empty area: | select enclosed submodules/connections |
| Del key | delete selected objects |
| Both editing modes: | |
| Right-click on module/submodule/connection: | popup menu |
| Double-click on submodule: | go into submodule |
| Click name label | edit name |
| Drag&drop module type from the tree view to the canvas | create a submodule of that type |
The activities of simple modules are implemented by the user. The algorithms are programmed in C++, using the OMNeT++ class library. The following sections contain a short introduction to discrete event simulation in general, how its concepts are implemented in OMNeT++, and gives an overview and practical advice on how to design and code simple modules.
This section contains a very brief introduction into how Discrete Event Simulation (DES) works, in order to introduce terms we'll use when explaining OMNeT++ concepts and implementation. If you're familiar with DES, you can skip this section.
A Discrete Event System is a system where state changes (events) happen at discrete points of time, and events take zero time to happen. It is assumed that nothing (i.e. nothing interesting) happens between two consecutive events, that is, no state change takes place in the system between the events (in contrast to continuous systems where state changes are continuous). Those systems that can be viewed as Discrete Event Systems can be modeled using Discrete Event Simulation. (Continuous systems are modelled using differential equations and suchlike.)
For example, computer networks are usually viewed as discrete event systems. Some of the events are:
This implies that between two events such as "start of a packet transmission" and "end of a packet transmission", nothing interesting happens. That is, the packet's state remains "being transmitted". Note that the definition of events and states always depends on the intent and purposes of the person doing the modeling. If we were interested in the transmission of individual bits, we would have included something like "start of bit transmission" and "end of bit transmission" among our events.
The time when events occur is often called event timestamp; with OMNeT++ we'll say arrival time (because in the class library, the word "timestamp" is reserved for a user-settable attribute in the event class). Time within the model is often called simulation time, model time or virtual time as opposed to real time or CPU time or which refers to how long the simulation program has been running or how much CPU time it has consumed.
Discrete event simulations maintain a set of future events, in a data structure often called FES (Future Event Set). Such simulators usually work according to the following pseudocode:
initialize -- this includes building the model and inserting initial events to FES
while (FES not empty and simulation not yet complete)
{
retrieve first event from FES
t:= timestamp of this event
process event (processing may insert new events in FES or delete existing ones)
}
finish simulation (write statistical results, etc.)
The first, initialization step usually builds the data structures representing the simulation model, calls any user-defined initialization code, and inserts initial events into the FES to ensure that the simulation can start. Initialization strategy can be quite different from one simulator to another.
The subsequent loop consumes events from the FES and processes them. Events are processed in strict timestamp order in order to maintain causality, that is, to ensure that no event may have an effect on earlier events.
Processing an event involves calls to user-supplied code. For example, using the computer network simulation example, processing a "timeout expired" event may consist of re-sending a copy of the network packet, updating the retry count, scheduling another "timeout" event, and so on. The user code may also remove events from the FES, for example when cancelling timeouts.
Simulation stops when there are no more events left (this happens rarely in practice), or when it isn't necessary for the simulation to run further because the model time or the CPU time has reached a given limit, or because the statistics have reached the desired accuracy. At this time, before the program exits, the simulation programmer will typically want to record statistics into output files.
The user creates simple module types are by subclassing the cSimpleModule class, which is part of the OMNeT++ class library. cSimpleModule, just as cCompoundModule, is derived from a common base class, cModule.
cSimpleModule, although stuffed with simulation-related functionality, doesn't do anything useful by itself. The simulation programmer has to redefine some virtual member functions to make it do useful work.
These member functions are the following:
In the initialization step, OMNeT++ builds the network: it creates the necessary simple and compound modules and connects them according to the NED definitions. OMNeT++ also calls the initialize() functions of all modules.
The activity() and handleMessage() functions are called during event processing. This means that the user will implement the model's behavior in these functions. Activity() and handleMessage() implement different event processing strategies: for each simple module, the user has to redefine exactly one of these functions. activity() is a coroutine-based solution which implements the process interaction approach (coroutines are non-preemptive [cooperative] threads), and handleMessage() is a function called for each event. Modules written with these functions can be freely mixed within a simulation model, so you can choose per-module basis.
The finish() functions are called when the simulation terminates successfully. It is the place of writing statistics.
All these functions will be discussed later in detail.
OMNeT++ uses messages to represent events. Each event is represented by an instance of the cMessage class or one its subclasses; there is no separate event class. Messages are sent from one module to another -- this means that the place where the "event will occur" is the message's destination module, and the model time when the event occurs is the arrival time of the message. Events like "timeout expired" are implemented with the module sending a message to itself.
Simulation time in OMNeT++ is stored in the C++ type simtime_t, which is a typedef for double.
Events are consumed from the FES in arrival time order, to maintain causality. More precisely, given two messages, the following rules apply:
Priority is a user-assigned integer attribute of messages.
Storing simulation time in doubles may sometimes cause inconveniences. Due to finite machine precision, two doubles calculated in two different ways do not always compare equal even if they theoretically should be. This means that if you want to explicitly rely on the arrival times of two events being the same, you should take care that simulation times which should be equal are calculated in exactly the same way. Another possible approach is to avoid equal arrival times, for example by adding/subtracting small values to schedule times to ensure specific execution order (inorder_epsilon).
We also thought about some simtime_precision parameter in the simulation kernel that would force t1 and t2 to be regarded equal if they are "very close" (if they differ less than simtime_precision). However, it is not at all clear how small simtime_precision should be; the mechanism incurs some run-time overhead; and all in all I'm not sure the whole thing would be of more benefit than trouble.
The implementation of the FES is a crucial factor in the performance of a discrete event simulator. In OMNeT++, the FES is implemented with binary heap, the most widely used data structure for this purpose. Heap is also the best algorithm we know, although exotic data structures like skiplist may perform better than heap in some cases. In case you're interested, the FES implementation is in the cMessageHeap class, but as a simulation programmer you won't ever need to care about it.
The C++ implementation of a simple module consists of:
For example, the C++ source for a Sliding Window Protocol implementation might look like this:
// file: swp.cc
#include <omnetpp.h>
// module class declaration:
class SlidingWindow : public cSimpleModule
{
Module_Class_Members( SlidingWindow,cSimpleModule,8192)
virtual void activity();
};
// module type registration:
Define_Module( SlidingWindow );
// implementation of the module class:
void SlidingWindow::activity()
{
int window_size = par("window_size");
...
}
In order to be able to refer to this simple module type in NED files, we should have an associated NED declaration which might look like this:
// file: swp.ned
simple SlidingWindow
parameters:
window_size: numeric const;
gates:
in: from_net, from_user;
out: to_net, to_user;
endsimple
The module declaration
Forms of module declaration
Module declarations can take two forms:
Define_Module(classname);
Define_Module_Like(classname, neddeclname);
The first form associates the class (subclassed from cSimpleModule) with the NED simple module declaration of the same name. For example, the
Declare_Module(SlidingWindow);
line would ensure that when you create an instance of SlidingWindow in your NED files, the module has the parameters and gates given in the simple SlidingWindow NED declaration, and the implementation will be an instance of the SlidingWindow C++ class.
The second form associates the class with a NED simple module declaration of a different name. You can use this form when you have several modules which share the same interface. This feature will be discussed in detail in the next section.
Header files
Module declarations should not be put into header files, because they are macros expanding to lines for which the compiler generates code.
Compound modules
All module types (including compound modules) need to have module declarations. For all compound modules, the NEDC compiler generates the Define_Module(..) lines automatically. However, it is your responsibility to put Define_Module(..) lines into one of the C++ sources for all your simple module types.
Implementation
Unless you are dying to learn about the dirty internals, you may just as well skip this section. But if you're interested, here it is: Define_Module (and also Define_Module_Like) is a macro which expands to a function definition plus the definition of a global object, something like this ugly code (luckily, you won't ever need to be interested in it):
static cModule *MyClass__create(const char *name, cModule *parentmod) {
return (cModule *) new MyClass(name, parentmod);
}
cModuleType MyClass__type("MyClass","MyClass",
(ModuleCreateFunc)MyClass__create);
The cModuleType object can act as a factory: it is able to create an instance of the given module type. This, together with the fact that all cModuleType objects are available in a single linked list, allows OMNeT++ to instantiate module types given only their class names as strings, without having to include the class declaration into any other C++ source.
The global object also stores the name of the NED interface associated with the module class. The interface description object (another object, generated by nedc) is looked up automatically at network construction time. Whenever a module of the given type is created, it will automatically have the parameters and gates specified in the associated interface description.
Suppose you have three different C++ module classes (TokenRing_MAC, Ethernet_MAC, FDDI_MAC) which have identical gates and parameters. Then you can create a single NED declaration, General_MAC for them and write the following module declarations in the C++ code:
Define_Module_Like( TokenRing_MAC, General_MAC); Define_Module_Like( Ethernet_MAC, General_MAC); Define_Module_Like( FDDI_MAC, General_MAC);
In this case, you won't be able to directly refer to the TokenRing_MAC, Ethernet_MAC, FDDI_MAC module types in your NED files. For example, you cannot write
module PC
submodules:
mac: Ethernet_MAC; // error: Ethernet_MAC not defined
...
endmodule
However, you can pass the module type in a string-valued parameter to the compound module:
module PC
parameters:
mac_type: string;
submodules:
mac: mac_type like General_MAC; // OK!
...
endmodule
The mac_type parameter should take the value "TokenRing_MAC", "Ethernet_MAC" or "FDDI_MAC", and a submodule of the appropriate type will be created. The value for the parameter can even be given in the ini file. This gives you a powerful tool to customize simulation models (see also Topology templates from the NED chapter).
As mentioned before, simple module classes have to be derived from cSimpleModule (either directly or indirectly). In addition to overwriting some of the previously mentioned four member functions (initialize(),activity(),handleMessage(),finish()), you have to write a constructor and some more functions. Some of this task can be automated, so when writing the C++ class declaration, you have two choices:
Using macro to declare the constructor
If you choose the first solution, you use the Module_Class_Members() macro:
Module_Class_Members( classname, baseclass, stacksize);
The first two arguments are obvious (baseclass is usually cSimpleModule), but stacksize needs some explanation. If you use activity(), the module code runs as a coroutine, so it will need a separate stack. (This will be discussed in detail later.)
As an example, the class declaration
class SlidingWindow : public cSimpleModule
{
Module_Class_Members( SlidingWindow,cSimpleModule,8192)
...
};
expands to something like this:
class SlidingWindow : public cSimpleModule
{
public:
SlidingWindow(const char *name, cModule *parentmodule,
unsigned stacksize = 8192) :
cSimpleModule(name, parentmodule, stacksize) {}
virtual const char *className() {return "SlidingWindow";}
...
};
Expanded form of the constructor
You will implement:
The advantage is that you get full control over the constructor, so you can initialize data members of the class (if you have any). You should not change the number or types of the arguments taken by the constructor, because it is called by OMNeT++-generated code. Also, remember to overwrite the className() function.
An example:
class TokenRing_MAC : public cSimpleModule
{
public:
cQueue queue; // a data member
TokenRing_MAC(const char *name, cModule *parentmodule,
unsigned stacksize = 8192);
virtual const char *className() {return "TokenRing_MAC";}
...
};
TokenRing_MAC(const char *name, cModule *parentmodule, unsigned stacksize) :
cSimpleModule(name, parentmodule, stacksize),
queue("queue") // initialize data member
{
}
Stack size decides between activity() and handleMessage()
If you make mistake (e.g. you forget to set zero stack size for a handleMessage() simple module): the default versions of the functions issue error messages telling you what is the problem.
It is usually a good idea to decompose a activity() or handleMessage() function when it grows too large. "Too large" is a matter of taste of course, but you should definitely consider splitting up the function if it is more that a few screens (say 50-100 lines) long. This will have a couple of advantages:
If you have variables which you want to access from all member functions (typically state variables are like that), you'll need to add those variables to the class as data members.
Let's see an example:
class TransportProtocol : public cSimpleModule
{
public:
Module_Class_Members(TransportProtocol, cSimpleModule, 8192)
int window_size;
int n_s; // N(s)
int n_r; // N(r)
cOutVector eedVector;
cStdDev eedStats;
//...
virtual void activity();
virtual void recalculateTimeout();
virtual void insertPacketIntoBuffer(cMessage *packet);
virtual void resendPacket(cMessage *packet);
//...
};
Define_Module( TransportProtocol );
void TransportProtocol::activity()
{
window_size = par("window_size");
n_s = n_r = 0;
eedVector.setName("End-to-End Delay");
eedStats.setName("eedStats");
//...
}
//...
Note that you may have to use the expanded form of the constructor (instead of Module_Class_Members()) to pass arguments to the constructors of member objects like eedVector and eedStats. But most often you don't need to go as far as that; for example, you can set parameters later from activity(), as shown in the example above.
To implement another variant of the Transport Protocol which uses a different timeout scheme, you could simply subclass TransportProtocol:
class AdvancedTransportProtocol : public TransportProtocol
{
public:
Module_Class_Members(AdvancedTransportProtocol,
TransportProtocol, 8192)
virtual void recalculateTimeout();
};
Define_Module( AdvancedTransportProtocol );
void AdvancedTransportProtocol::recalculateTimeout()
{
//...
}
This section discusses cSimpleModule's four previously mentioned member functions, intended to be redefined by the user: initialize(), activity(), handleMessage() and finish().
Process-style description
With activity(), you can code the simple module much like you would code an operating system process or a thread. You can wait for an incoming message (event) at any point of the code, you can suspend the execution for some time (model time!), etc. When the activity() function exits, the module is terminated. (The simulation can continue if there are other modules which can run.)
The most important functions you can use in activity() are (they will be discussed in detail later):
The activity() function normally contains an infinite loop, with at least a wait() or receive() call in its body.
Examples:
TBD
Application area
One area where the process-style description is especially convenient is when the process has many states but transitions are very limited, ie. from any state the process can only go to one or two other states. For example, this is the case when programming a network application which uses a single network connection. The pseudocode of the application which talks to a transport layer protocol might look like this:
activity()
{
while(true)
{
open connection by sending OPEN command to transport layer
receive reply from transport layer
if (open not successful)
{
wait(some time)
continue // loop back to while()
}
while(there's more to do)
{
send data on network connection
if (connection broken)
{
continue outer loop // loop back to outer while()
}
wait(some time)
receive data on network connection
if (connection broken)
{
continue outer loop // loop back to outer while()
}
wait(some time)
}
close connection by sending CLOSE command to transport layer
if (close not successful)
{
// handle error
}
wait(some time)
}
}
If you want to handle several connections simultaneously, you may dynamically create as instances of the simple module above as needed. Dynamic module creation will be discussed later.
Activity() is run as a coroutine
Activity() is run in a coroutine. Coroutines are a sort of threads which are scheduled non-preemptively (this is also called cooperative multitasking). From one coroutine you can switch to another coroutine by a transferTo(otherCoroutine) call. Then this coroutine is suspended and otherCoroutine will run. Later, when otherCoroutine does a transferTo(firstCoroutine) call, execution of the first coroutine will resume from the point of the transferTo(otherCoroutine) call. The full state of the coroutine, including local variables are preserved while the thread of execution is in another coroutines. This implies that each coroutine must have an own processor stack, and transferTo() involves a switch from one processor stack to another.
Coroutines are at the heart of OMNeT++, and the simulation programmer doesn't ever need to call transferTo() or other functions in the coroutine library, nor does he need to care about the coroutine library implementation. But it is important to understand how the event loop found in discrete event simulators works with coroutines.
When using coroutines, the event loop looks like this (simplified):
while (FES not empty and simulation not yet complete)
{
retrieve first event from FES
t:= timestamp of this event
transferTo(module containing the event)
}
That is, when the module has an event, the simulation kernel transfers the control to the module's coroutine. It is expected that when the module "decides it has finished the processing of the event", it will transfer the control back to the simulation kernel by a transferTo(main) call. Initially, simple modules using activity() are "booted" by events ("starter messages") inserted into the FES by the simulation kernel before the start of the simulation.
How does the coroutine know it has "finished processing the event"? The answer: when it requests another event. The functions which request events from the simulation kernel are the receive..() family and wait(), so their implementations contain a transferTo(main) call somewhere.
Their pseudocode, as implemented in OMNeT++:
receiveNew() // other receive...() variations are similar
{
transferTo(main)
retrieve current event
return the event // remember: events = messages
}
wait()
{
create an event e and schedule it at (current sim. time + wait interval)
while(true) {
transferTo(main)
retrieve current event
if (current event is e)
break from loop
else
store current event for later use (in the "put-aside queue")
}
delete event e
return
}
Thus, the receive...() and wait() calls are special points in the activity() function, because that's where:
Starter messages
Modules written with activity() need starter messages to "boot". These starter messages are inserted into the FES automatically by OMNeT++ at the beginning of the simulation, even before the initialize() functions are called.
Coroutine stack size
All the simulation programmer needs to care about coroutines is to choose the processor stack size for them. This cannot be automated (Eerrr... at least not without hardware support, some trick with virtual memory handling).
8 or 16 kbytes is usually a good choice, but you may need more if the module uses recursive functions or has local variables which occupy a lot of stack space. OMNeT++ has a built-mechanism that will usually detect if the module stack is too small and overflows. OMNeT++ can also tell you how much stack space a module actually uses, so you can find it out if you overestimated the stack needs.
initialize() and finish() with activity()
Because local variables of activity() are preserved across events, you can store everything (state information, packet buffers, etc.) in them. Local variables can be initialized at the top of the activity() function, so there isn't much need to use initialize().
However, you need finish() if you want to write statistics at the end of the simulation. And because finish() cannot access the local variables of activity(), you have to put the variables and objects that contain the statistics into the module class. You still don't need initialize() because class members can also be initialized at the top of activity().
Thus, a typical setup looks like this pseudocode:
class MySimpleModule ...
{
...
variables for statistics collection
activity();
finish();
};
MySimpleModule::activity()
{
declare local vars and initialize them
initialize statistics collection variables
while(true)
{
...
}
}
MySimpleModule::finish()
{
record statistics into file
}
Advantages and drawbacks
Advantages:
Drawbacks:
Other simulators
Coroutines are used by a number of other simulation packages:
Function called for each event
The idea is that at each event we simply call a user-defined function instead of switching to a coroutine that has activity() running in it. The "user-defined function" is the handleMessage(cMessage *msg) virtual member function of cSimpleModule; the user has to redefine the function to make it do useful work. Calls to handleMessage() occur in the main stack of the program -- no coroutine stack is needed and no context switch is done.
The handleMessage() function will be called for every message that arrives at the module. The function should process the message and return immediately after that. The simulation time is potentially different in each call. No simulation time elapses within a call to handleMessage().
The pseudocode of the event loop which is able to handle both activity() and handleMessage() simple modules:
while (FES not empty and simulation not yet complete)
{
retrieve first event from FES
t:= timestamp of this event
m:= module containing this event
if (m works with handleMessage())
m->handleMessage( event )
else // m works with activity()
transferTo( m )
}
Modules with handleMessage() are NOT started automatically: the simulation kernel creates starter messages only for modules with activity(). This means that you have to schedule self-messages from the initialize() function if you want the handleMessage() simple module to start working "by itself", without first receiving a message from other modules.
Programming with handleMessage()
To use the handleMessage() mechanism in a simple module, you must specify zero stack size for the module. This is important, because this tells OMNeT++ that you want to use handleMessage() and not activity().
Message/event related functions you can use in handleMessage():
You cannot use the receive...() family and wait() functions in handleMessage(), because they are coroutine-based by nature, as explained in the section about activity(). You also cannot use end() because its job is to terminate the coroutine.
You have to add data members to the module class for every piece of information you want to preserve. This information cannot be stored in local variables of handleMessage() because they are destroyed when the function returns. Also, they cannot be stored in static variables in the function (or the class), because they would be shared between all instances of the class.
Data members to be added to the module class will typically include things like:
You can initialize these variables from the initialize() function. The constructor is not a very good place for this purpose because it is called in the network setup phase when the model is still under construction, so a lot of information you may want to use is not yet available then.
Another task you have to do in initialize() is to schedule initial event(s) which trigger the first call(s) to handleMessage(). After the first call, handleMessage() must take care to schedule further events for itself so that the "chain" is not broken. Scheduling events is not necessary if your module only has to react to messages coming from other modules.
finish() is used in the normal way: to record statistics information accumulated in data members of the class at the end of the simulation.
Application area
There are two areas where handleMessage() is definitely a better choice than activity():
There's also a good rule of thumb. If your module, programmed with activity(), looks like this:
activity()
{
initialization code
while(true)
{
msg = receive();
arbitrary code which doesn't contain any receive() or wait() calls
}
}
Then it can be trivially converted to handleMessage():
initialize()
{
initialization code
}
handleMessage( msg )
{
arbitrary code which doesn't contain any receive() or wait() calls
}
Example 1: Simple traffic generators and sinks
The code for simple packet generators and sinks programmed with handleMessage()might be as simple as this:
PacketGenerator::handleMessage(m)
{
create and send out packet
schedule m again to trigger next call to handleMessage // (self-message)
}
PacketSink::handleMessage(m)
{
delete m
}
Note that PacketGenerator will need to redefine initialize() to create m and schedule the first event.
The following simple module generates packets with exponential inter-arrival time. (Some details in the source haven't been discussed yet, but the code is probably understandable nevertheless.)
class Generator : public cSimpleModule
{
Module_Class_Members(Generator,cSimpleModule,0)
// note zero stack size!
virtual void initialize();
virtual void handleMessage(cMessage *msg);
};
Define_Module( Generator );
void Generator::initialize()
{
// schedule first sending
scheduleAt(simTime(), new cMessage);
}
void Generator::handleMessage(cMessage *msg)
{
// generate & send packet
cMessage *pkt = new cMessage;
send(pkt, "out");
// schedule next call
scheduleAt(simTime()+exponential(1.0), msg);
}
Example 2: Bursty traffic generator
A bit more realistic example is to rewrite our Generator to create packet bursts, each consisting of burst_length packets.
We add some data members to the class:
The code:
class BurstyGenerator : public cSimpleModule
{
Module_Class_Members(Generator,cSimpleModule,0)
// note the zero stack size!
int burst_length;
int burst_ctr;
virtual void initialize();
virtual void handleMessage(cMessage *msg);
};
Define_Module( BurstyGenerator );
void BurstyGenerator::initialize()
{
// init parameters and state variables
burst_length = par("burst_length");
burst_ctr = burst_length;
// schedule first packet of first burst
scheduleAt(simTime(), new cMessage);
}
void BurstyGenerator::handleMessage(cMessage *msg)
{
// generate & send packet
cMessage *pkt = new cMessage;
send(pkt, "out");
// if this was the last packet of the burst
if (--burst_ctr == 0)
{
// schedule next burst
burst_ctr = burst_length;
scheduleAt(simTime()+exponential(5.0), msg);
}
else
{
// schedule next sending within burst
scheduleAt(simTime()+exponential(1.0), msg);
}
}
Advantages and drawbacks
Advantages:
Drawbacks:
Other simulators
Many simulation packages use a similar approach, often topped with something like a state machine (FSM) which hides the underlying function calls. Such systems are:
OMNeT++'s FSM support is described in the next section.
Purpose
initialize() - to provide place for any user setup code
finish() - to provide place where the user can record statistics after the simulation has completed
When and how they are called
The initialize() functions of the modules are invoked before the first event is processed, but after the initial events (starter messages) have been placed into the FES by the simulation kernel.
Both simple and compound modules have initialize() functions. A compound module has its initialize() function called before all its submodules have.
The finish() functions are called when the event loop has terminated, and only if it terminated normally (i.e. not with a runtime error). The calling order is the reverse as with initialize(): first submodules, then the containing compound module. (The bottom line is that in the moment there's no "official" possibility to redefine initialize() and finish() for compound modules; the unofficial way is to write into the nedc-generated C++ code. Future versions of OMNeT++ will support adding these functions to compound modules.)
This is summarized in the following pseudocode (although you won't find this code "as is" in the simulation kernel sources):
perform simulation run:
build network (i.e. the system module and its submodules recursively)
insert starter messages for all submodules using activity()
do callInitialize() on system module
enter event loop // (described earlier)
if (event loop terminated normally) // i.e. not with a runtime error
do callFinish() on system module
clean up
callInitialize()
{
call to user-defined initialize() function
if (module is compound)
for (each submodule)
do callInitialize() on submodule
}
callFinish()
{
if (module is compound)
for (each submodule)
do callFinish() on submodule
call to user-defined finish() function
}
initialize() vs. constructor
Usually you should not put simulation-related code into the simple module constructor. For example, modules often need to investigate their surroundings (maybe the whole network) at the beginning of the simulation and save the collected info into internal tables. Code like that cannot be placed into the constructor since the network is still being set up when the constructor is called.
finish() vs. destructor
Keep in mind that finish() is not always called, so it isn't a good place for cleanup code which should run every time the module is deleted. finish() is only a good place for writing statistics, result post-processing and other stuff which are to run only on successful completion.
Cleanup code should go into the destructor. But in fact, you almost never need to write a destructor because OMNeT++ keeps track of objects you create and disposes of them automatically (sort of automatic garbage collection). However it cannot track objects not derived from cObject (see later), so they may need to be deleted manually from the destructor.
Multi-stage initialization(NEW)
In simulation models, when one-stage initialization provided by initialize() is not sufficient, one can use multi-stage initialization. Modules have two functions which can be redefined by the user:
void initialize(int stage); int numInitStages();
At the beginning of the simulation, initialize(0) is called for all modules, then initialize(1), initialize(2), etc. For each module, numInitStages() must be redefined to return the number of init stages required, e.g. for a two-stage init, numInitStages() should return 2, and initialize(int stage) must be implemented to handle the stage=0 and stage=1 cases.
The callInitialize() function performs the full multi-stage initialization for that module and all its submodules.
If you do not redefine the multi-stage initialization functions, the default behavior is single-stage initialization: the default numInitStages() returns 1, and the default initialize(int stage) simply calls initialize().
"end-of-simulation event"
The task of finish() is solved in many simulators (e.g. OPNET) by introducing a special end-of-simulation event. This is not a very good practice because the simulation programmer has to code the algorithms (often FSMs) so that they can always properly respond to end-of-simulation events, in whichever state they are. This often makes program code unnecessarily complicated.
This fact is also evidenced in the design of the Parsec simulation language (UCLA). Its predecessor Maisie used end-of-simulation events, but -- as documented in the Parsec manual - this is led to awkward programming in many cases, so for Parsec, end-of-simulation events were dropped in favour of finish() (called finalize() in Parsec).
Overview
Finite State Machines (FSMs) can make life with handleMessage() easier. OMNeT++ provides a class and a set of macros to build FSMs. OMNeT++'s FSMs work very much like OPNET's or SDL's.
The key points are:
OMNeT++'s FSMs can be nested. This means that any state (or rather, its entry or exit code) may contain a further full-fledged FSM_Switch (see below). This allows you to introduce sub-states and thereby bring some structure into the state space if it would become too large.
The FSM API
FSM state is stored in an object of type cFSM. The possible states are defined by an enum; the enum is also a place to tell which state is transient and which is steady. In the following example, SLEEP and ACTIVE are steady states and SEND is transient (the numbers in parens must be unique within the state type and they are used for constructing the numeric IDs for the states):
enum {
INIT = 0,
SLEEP = FSM_Steady(1),
ACTIVE = FSM_Steady(2),
SEND = FSM_Transient(1),
};
The actual FSM is embedded in a switch-like statement, FSM_Switch(), where you have cases for entering and leaving each state:
FSM_Switch(fsm)
{
case FSM_Exit(state1):
//...
break;
case FSM_Enter(state1):
//...
break;
case FSM_Exit(state2):
//...
break;
case FSM_Enter(state2):
//...
break;
//...
};
State transitions are done via calls to FSM_Goto(), which simply stores the new state in the cFSM object:
FSM_Goto(fsm,newState);
The FSM starts from the state with the numeric code 0; this state is conventionally named INIT.
Debugging FSMs
If you #define FSM_DEBUG before including omnetpp.h, each state transition will be logged to ev:
#define FSM_DEBUG #include <omnetpp.h>
The actual printing is done through the FSM_Print() macro. You might redefine it if you don't like what it currently does:
#define FSM_Print(fsm,exiting) \
(ev << "FSM " << (fsm).name() \
<< ((exiting) ? ": exiting " : ": entering ") \
<< (fsm).stateName() << endl)
Implementation
The FSM_Switch() is a macro. It expands to a switch() statement embedded in a for() loop which repeats until the FSM reaches a steady state. (The actual code is rather ugly, but if you're dying to see it, it's in cfsm.h.)
Infinite loops are avoided by counting state transitions: if an FSM goes through 64 transitions without reaching a steady state, the simulation will terminate with an error message.
An example
Let us write another flavour of a bursty generator. It has two states, SLEEP and ACTIVE. In the SLEEP state, the module does nothing. In the ACTIVE state, it sends messages with a given inter-arrival time. The code was taken from the fifo2 sample simulation.
#define FSM_DEBUG
#include <omnetpp.h>
class BurstyGenerator : public cSimpleModule
{
public:
Module_Class_Members(BurstyGenerator,cSimpleModule,0)
// parameters
double sleepTimeMean;
double burstTimeMean;
double sendIATime;
cPar *msgLength;
// FSM and its states
cFSM fsm;
enum {
INIT = 0,
SLEEP = FSM_Steady(1),
ACTIVE = FSM_Steady(2),
SEND = FSM_Transient(1),
};
// variables used
int i;
cMessage *startStopBurst;
cMessage *sendMessage;
// the virtual functions
virtual void initialize();
virtual void handleMessage(cMessage *msg);
};
Define_Module( BurstyGenerator );
void BurstyGenerator::initialize()
{
fsm.setName("fsm");
sleepTimeMean = par("sleep_time_mean");
burstTimeMean = par("burst_time_mean");
sendIATime = par("send_ia_time");
msgLength = &par("msg_length");
i = 0;
WATCH(i); // always put watches in initialize()
startStopBurst = new cMessage("startStopBurst");
sendMessage = new cMessage("sendMessage");
scheduleAt(0.0,startStopBurst);
}
void BurstyGenerator::handleMessage(cMessage *msg)
{
FSM_Switch(fsm)
{
case FSM_Exit(INIT):
// transition to SLEEP state
FSM_Goto(fsm,SLEEP);
break;
case FSM_Enter(SLEEP):
// schedule end of sleep period (start of next burst)
scheduleAt(simTime()+exponential(sleepTimeMean),
startStopBurst);
break;
case FSM_Exit(SLEEP):
// schedule end of this burst
scheduleAt(simTime()+exponential(burstTimeMean),
startStopBurst);
// transition to ACTIVE state:
if (msg!=startStopBurst)
error("invalid event in state ACTIVE");
FSM_Goto(fsm,ACTIVE);
break;
case FSM_Enter(ACTIVE):
// schedule next sending
scheduleAt(simTime()+exponential(sendIATime), sendMessage);
break;
case FSM_Exit(ACTIVE):
// transition to either SEND or SLEEP
if (msg==sendMessage) {
FSM_Goto(fsm,SEND);
} else if (msg==startStopBurst) {
cancelEvent(sendMessage);
FSM_Goto(fsm,SLEEP);
} else
error("invalid event in state ACTIVE");
break;
case FSM_Exit(SEND):
{
// generate and send out job
char msgname[32];
sprintf( msgname, "job-0", ++i);
ev << "Generating " << msgname << endl;
cMessage *job = new cMessage(msgname);
job->setLength( (long) *msgLength );
job->setTimestamp();
send( job, "out" );
// return to ACTIVE
FSM_Goto(fsm,ACTIVE);
break;
}
}
}
Data rate modeling
If data rate is specified for a connection, a message will have a certain nonzero transmission time, depending on its length. This means that when a message is sent out through an output gate, the message "reserves" the gate for a given period ("it is being transmitted").
While a message is under transmission, other messages have to wait until the transmission finishes. You can still use send() while the gate is busy, but the message's arrival will be delayed; just like the gate had an internal queue for the messages waiting to be transmitted.
The OMNeT++ class library provides you with functions to check whether a certain output gate is transmitting or to learn when it finishes transmission.
If the connection with a data rate is not the immediate one connected to the simple module's output gate but the second one in the route, you have to check the second gate's busy condition.
Implementation of message sending
Message sending is implemented in the following way: the arrival time and the bit error flag of a message are calculated at once, when the send() (or similar) function is invoked. That is, if the message travels through several links until it reaches its destination, it is not scheduled individually for each link, but rather, every calculation is done once, within the send() call. This implementation was chosen because of its run-time efficiency.
In the actual implementation of queuing the messages at busy gates and modeling the transmission delay, messages do not actually queue up in gates; gates do not have internal queues. Instead, as the time when each gate will finish transmission is known at the time of sending the message, the arrival time of the message can be calculated in advance. Then the message will be stored in the event queue (FES) until the simulation time advances to its arrival time and it is retrieved by its destination module.
TBD add pseudocode
Consequence
The implementation has the following consequence. If you change the delay (or the bit error rate, or the data rate) of a link during simulation, the modeling of messages sent "just before" the parameter change will not be accurate. Namely, if link parameters change while a message is "under way" in the model, that message will not be affected by the parameter change, although it should. However, all subsequent messages will be modelled correctly. Similar for data rate: if a data rate changes during the simulation, the change will affect only the messages that are sent after the change.
If it is important to model gates and channels with changing properties, you can go two ways:
The approach of some other simulators
Note that some simulators (e.g. OPNET) assign packet queues to input gates (ports), and messages send are buffered at the destination module (or the remote end of the link) until received by the destination module. With that approach, events and messages are separate entities, that is, a send operation includes placing the message in the packet queue and scheduling an event which will signal the arrival of the packet. In some implementations, also output gates have packet queues where packets wait until the channel becomes free (available for transmission).
OMNeT++ gates don't have associated queues. The place where the sent but not yet received messages are buffered is the FES. OMNeT++'s approach is potentially faster than the above mentioned solution because it doesn't have the enqueue/dequeue overhead and also spares an event creation. The drawback is, as mentioned above, that changes to channel parameters do not take effect immediately.
Here's a bunch of advice on how to write OMNeT++ models. Some of them are "rules of thumb", saying if you program like that, you're likely to have less trouble; other conventions are aimed at making the models produced by the OMNeT++ community more consistent.
Conventions for writing simple modules:
Because of the structure of the simulation system, one can create libraries of reusable elements in several ways. The three basic types are:
The elegant thing is that the user of the library does not need to know which kind of library he/she is using; the three types of libraries are equivalent in terms of usage.
Simple modules that can be used in more than simulations can form an object library. Good candidates for module libraries are simple modules that implement:
To create a library, you compile the simple module C++ sources and collect the object files in one directory. You'll also need to the provide the NED descriptions:
library/generator.ned
generator.o
sink.ned
sink.o
ethernet.ned
ethernet.o
The NED files contain the interfaces of the simple modules. For example:
// generator.ned
simple Generator
parameters:
interarrival time, message_length, message_kind;
gates:
out: output;
endsimple
The user of the library would include generator.ned in his/her NED files, and link the executable with generator.o. This is more or less the same concept as conventional C/C++ header files and libraries. The basic advantage is the same as with C/C++: you save compilation time and hide concrete implementation. The latter also means that you can give the module library to others without having to share the C++ source.
It could also be meaningful to provide the C++ header files with the module class declarations. This would enable the user to directly call the member functions of the module object from the simulation program and derive new module classes by redefining the virtual functions.
You do not need to have a separate NED file for each module: you could merge all of them into a single library.ned that contains the NED declarations of all modules without all side effects. However, it is not recommended to put all object files into one library (.a or .lib), because then every simple module would be present in simulation programs linked with the library, regardless whether the simulation uses them or not.
The NED sources of reusable compound modules can also be placed in a library. Candidates are:
The NED sources are used through the import mechanism; the corresponding simple module object files still to be linked in the executable.
The user does not necessarily notice that he/she is using a compound module library and not a simple module library. In NED files, the user imports and uses the compound module sources in exactly the same way as he/she used the simple module interface declarations. Linking also goes in the same way; if the simple modules objects necessary for a certain compound module are aggregated into a library (.a or .lib), the user does not even notice the difference from the number of files he/she has to link in.
If you share a compound module with others, you do not necessarily have to share the NED source and reveal the internals of the compound module. You can turn the compound module into something that very much looks like a simple module.
Suppose you have the following compound module:
// router-compound.ned
module Router:
parameters:
processing_delay, buffersize;
gates:
in: input_ports[];
out: output_ports[];
submodules:
routing: RoutingModule
parameters:
//...
gatesizes:
//...
datalink: DataLink[num_of_ports]
parameters:
retry_count = 5,
window_size = 2;
//...
connections:
// ...
endmodule
First, you would compile this NED file with the NEDC compiler and the resulting C++ code with the C++ compiler. Then you would aggregate this object file with the simple module object files into a single library (.a or .lib). Also, you would write a separate NED file that declares the interface of the new "simple" module:
// router-simple.ned simple Router: parameters: processing_delay, buffersize; gates: in: input_ports[]; out: output_ports[]; endsimple
The method produced a precompiled compound module. The resulting two files can be placed into a simple module library and can be used identically to ordinary simple modules.
Using precompiled compound modules you can hide the internal complexity of your model from direct inspection. However, nothing can prevent a user from building a simulation executable with it and exploring the structure of your compound module using OMNeT++ simulation kernel functions. Consequently, using precompiled compound modules is more useful as a structuring tool.
The hierarchical module structure of OMNeT++ allows you to organize the model around different levels:
Physical topology:
Within a node:
The advantage of OMNeT++ over many existing simulators is that the depth of the module nesting is not limited, and, what is in connection with the previous one, that a simple module can be transformed into a compound module by splitting the code into several simple modules without affecting existing users of the module and vica versa. The latter means that the programmer of the model is not under pressure from possibly incorrect early design decisions about what to implement with a single module and what with a compound module.
One can make use of flexible model topologies. It is straightforward to create ring, mesh, butterfly, torus, hypercube, tree, fat tree and other topologies with conditional loop connections.
Furthermore, general topology templates (e.g. mesh or hypercube) can be created, where the types of the actual nodes are left as parameters. The actual node types are substituted as parameter values for each concrete simulation. Topology templates could be placed in a library and imported from there if needed.
Tuning means finding the parameter values which produce optimal operation of the system. In OMNeT++, you can tune the model during runtime. The code that monitors performance and changes parameter values can be placed:
OMNeT++ supports the model tuning concept by providing reference parameters. Parameters that influence the model performance and need to be tuned will be declared at the highest layer and taken by both the model and the monitor part.
An example of model tuning is how one can determine the critical throughput of a communication network by changing the offered load according to performance measures of the network (queuing times etc.)
One might need to perform a large number of simulation runs where the model parameters are not known in advance. This can be the case when one wants to optimize a system and parameter tuning cannot be used because
In this case, the following solution be followed. The network would consist of only one simple module that would organize the simulation runs by creating, running and destroying the actual models with each experiment. The simple module's code would look like this:
SimulationManager::activity()
{
determine parameters for the first run
while(true)
{
create the model (a compound module) with the current run parameters
schedule
wait( some time) // while the model runs
delete future events that belong to the model
get statistics out of the model
destroy the model
if (simulation is done)
break
calculate parameters for the next run
}
write out results
}
The solutions built into OMNeT++ (flexible module topologies, dynamic creation of compound modules etc.) strongly support this concept.
Dynamic topology optimization is the generalization of the "parameter tuning" and "multiple experiments within one simulation run" concepts. If one wants to simulate large systems, it is possible that one part of the model needs its topology to be optimized (optimal number of servers, optimal interconnection etc.) while other parts of the model have reached their steady state and should not be bothered.
This can be achieved by modifying the previous scheme. Parts of
the model that do not need topology optimization can be created
once and left running for the whole duration of the simulation;
other parts are examined and their structure is modified from
time to time.
OMNeT++ has a rich C++ class library which you can use when implementing simple modules. A quick overview of the areas supported by the simulation library:
Base class
Classes in the OMNeT++ simulation library are derived from cObject. Functionality and conventions that come from cObject:
Classes inherit and redefine several cObject member functions; in the following we'll discuss some of the practically important ones.
Setting and getting attributes
Member functions that set and query object attributes follow consistent naming. The setter member function has the form setSomething(...) and its getter counterpart is named something(), i.e. the "get" verb found in Java and some other libraries is omitted for brevity. For example, the length attribute of the cMessage class can be set and read like this:
msg->setLength( 1024 ); length = msg->length();
className()
For each class, the className() member function returns the class name as a string:
const char *classname = msg->className(); // returns "cMessage"
Name attribute
An object can be assigned a name (a character string). The name string is the first argument to the constructor of every class, and it defaults to NULL (no name string). If you supply a name string, the object will make its own copy (strdup). As an example, you can create a message object like this:
cMessage *mymsg = new cMessage("mymsg");
You can also set the name after the object has been created:
mymsg->setName("mymsg");
You can get a pointer to the internally stored copy of the name string like this:
const char *name = mymsg->name(); // --> returns ptr to internal copy
// of "mymsg"
For convenience and efficiency reasons, the empty string "" and NULL are treated as equivalent by library objects: "" is stored as NULL (so that it does not consume heap), but it is returned as "" (so that it is easier to print out etc). Thus, if you create a message object with either NULL or "" as name, it will be stored as NULL and name() will return a pointer to "", a static string:
cMessage *msg = new cMessage(NULL, <additional args>); const char *str = msg->name(); // --> returns ptr to ""
fullName() and fullPath()
Objects have two more member functions which return other sort of names based on the name attribute: fullName() and fullPath().
Suppose we have a module in the network university_lan, compound module fddi_ring, simple module station[10]. If you call the functions on the simple module object (cSimpleModule inherits from cObject, too), the functions will return these values:
ev << module->name(); // --> "station" ev << module->fullName(); // --> "station[10]" ev << module->fullPath(); // --> "university_lan.fddi_ring.station[10]"
These functions work for any object. For example, a local object inside the module would produce results like this:
void FDDIStation::activity()
{
cQueue buffer("buffer");
ev << buffer->fullPath(); // --> "university_lan.fddi_ring.
// station[10].buffer"
}
fullName() and fullPath(), together with className() can be used for example to generate informative error messages.
Be aware that fullName() and fullPath() return pointers to static buffers. Each call will overwrite the previous content of the buffer, so for example you shouldn't put two calls in a single printf() statement:
ev.printf("object1 is '', object2 is ''\n",
object1->fullPath(),
object2->fullPath()
); // WRONG! Same string is printed twice!!!
Copying and duplicating objects
The dup() member function creates an exact copy of the object, duplicating contained objects also if necessary. This is especially useful in the case of message objects. dup() returns a pointer of type cObject *, so it needs to be cast to the proper type:
cMessage *copyMsg = (cMessage *) msg->dup();
dup() works through calling the copy constructor, which in turn relies on the assignment operator between objects. operator=() can be used to copy contents of an object into another object of the same type. The copying is done properly; object contained in the object will also be duplicated if necessary. For various reasons, operator=() does not copy the name string; the copy constructor does it.
Iterators
There are several container classes in the library (cQueue, cArray etc.) For many of them, there is a corresponding iterator class that you can use to loop through the objects stored in the container. For example:
cQueue queue;
//..
for (cQueueIterator queueIter(queue); !queueIter.end(); queueIter++)
{
cObject *containedObject = queueIter();
}
Ownership control
By default, if a container object is destroyed, it destroys the contained objects too. If you call dup(), the contained objects are duplicated too for the new container. This is done so because contained objects are owned by the container; ownership is defined as the right/duty of deallocation. However, there is a fine-grain ownership control mechanism built in which allows you to specify on per-object basis whether you want objects to be owned by the container or not; by calling the takeOwnership() member function with false, you tell the container that you don't want it to become the owner of objects that will be inserted in the future. You can explicitly select an owner for any object by calling its setOwner() member function.
Tracing
The tracing feature will be used extensively in the code examples, so it is shortly introduced here. It will be covered in detail in a later section.
The ev object represents the user interface of the simulation. You can send debugging output to ev with the C++-style output operators:
ev << "packet received, sequence number is " << seq_num << endl;
An alternative solution is ev.printf():
ev.printf("packet received, sequence number is 0\n",seq_num);
Simulation time conversion
There are utility functions which convert simulation time (simtime_t) to a printable string (like "3s 130ms 230us") and vica versa.
The simtimeToStr() function converts a simtime_t (passed in the first arg) to textual form. The result is placed into the buffer pointed to by the second arg. If the second arg is omitted or it is NULL, simtimeToStr() will place the result into a static buffer which is overwritten with each call:
char buf[32];
ev.printf("t1=, t2=\n", simtimeToStr(t1), simTimeToStr(t2,buf));
The strToSimtime() function parses a time specification passed in a string, and returns a simtime_t. If the string cannot be entirely interpreted, -1 is returned:
simtime_t t = strToSimtime("30s 152ms");
Another variant, strToSimtime0()can be used if the time string is a substring in a larger string. Instead of taking a char*, it takes a reference to char* (char*&) as the first argument. The function sets the pointer to the first character that could not be interpreted as part of the time string, and returns the value. It never returns -1; if nothing at the beginning of the string looked like simulation time, it returns 0.
const char *s = "30s 152ms and some rubbish"; simtime_t t = strToSimtime0(s); // now s points to "and some rubbish"
Utility <string.h> functions
The opp_strdup(), opp_strcpy(), opp_strcmp() functions are the same as their <string.h> equivalents, except that they treat NULL and the empty string ("") as identical, and opp_strdup() uses operator new instead of malloc().
The opp_concat() function might also be useful, for example in constructing object names. It takes up to four const char * pointers, concatenates them in a static buffer and returns pointer to the result. The result's length shouldn't exceed 255 characters.
In OMNeT++, cMessage is a central class. Objects of cMessage and subclasses may model a number of things: events; messages; packets, frames, cells, bits or signals travelling in a network; entities travelling in a system and so on.
Attributes
A cMessage object has number of attributes. Some are used by the simulation kernel, others are provided just for the convenience of the simulation programmer. A more-or-less complete list:
Basic usage
A cMessage object can be created in the following way:
cMessage *msg = new cMessage( "msg-name", kind, length, priority, errorflag);
The kind, length, and priority are integers, and errorflag is boolean. All arguments have default values, so the following initializations are also valid:
cMessage *msg1 = new cMessage;
cMessage *msg2 = new cMessage("data-packet", DATAPACKET_KIND, 8*1500 );
Once a message has been created, its data members can be changed by the following functions:
msg->setKind( kind ); msg->setLength( length ); msg->setPriority( priority ); msg->setBitError( err ); msg->setTimestamp(); msg->setTimestamp( simtime );
With these functions the user can set the message kind, the message length, the priority, the error flag and the time stamp. The setTimeStamp() function without any argument sets the time stamp to the current simulation time.
The values can be obtained by the following functions:
int k = msg->kind(); int p = msg->priority(); int l = msg->length(); bool b = msg->hasBitError(); simtime_t t = msg->timestamp();
Duplicating messages
It is often needed to duplicate a message (for example, send one and keep a copy). This can be done in the standard ways as for any other OMNeT++ object:
cMessage *copy1 = (cMessage *) msg->dup(); cMessage *copy2 = new cMessage( *msg );
The two are equivalent. The resulting message is an exact copy of the original, including message parameters (cPar or other object types) and encapsulated messages.
Adding, setting and reading parameters
You can add any number of parameters to a cMessage object. Parameters are objects of cPar type. You add a new parameter to the message with the addPar() member function:
msg->addPar("dest_addr");
You can get back the reference to the parameter object with the par() member function, and because cPar supports typecasting and assignment, it is easy to read and set the value of a parameter:
long dest_addr = msg->par("dest_addr");
msg->par("dest_addr") = 168;
The addPar() function also returns a reference to the added cPar object, so you can set the value of the new parameter at the same place:
msg->addPar("dest_addr") = 168;
You can use the hasPar() function to see if the message has a given parameter or not:
if (!msg->hasPar("dest_addr"))
msg->addPar("dest_addr");
Numeric indices
Message parameters can be accessed also by index in the parameter array. The findPar() function returns the index of a parameter or -1 if the parameter cannot be found. The parameter can then be accessed using an overloaded par() function. Access by index is more efficient than access by name (although access by name might become faster in the future by using hashtables):
long dest_addr = 0;
int index = msg->findPar("dest_addr");
if (index>=0)
dest_addr = msg->par(index);
Adding arbitrary data by accessing the internal array
Message parameters are stored in an object of type cArray which can store any object type not only cPars. The parList() member function lets you directly access the internal cArray, so by calling cArray's member functions you can attach any object to the message. An example:
cLongHistogram *pklen_distr = new cLongHistogram("pklen_distr");
msg->parList().add( pklen_distr );
...
cLongHistogram *pklen_distr =
(cLongHistogram *) msg->parList().get("pklen_distr");
You should take care that names of the attached objects do not clash with parameter names.
If you do not add parameters to the message and do not call the parList() function, the internal cArray object will not be created. This saves you both storage and execution time.
You can attach non-object types (or non-cObject objects) to the message by using cPar's void* pointer 'P') type (see later in the description of cPar). An example:
struct conn_t *conn = new conn_t; // conn_t is a C struct
msg->addPar("conn") = (void *) conn;
msg->par("conn").configPointer(NULL,NULL,sizeof(struct conn_t));
Runtime overhead
It has been reported that using cPar message parameters might account for quite a large part of execution time (sometimes as much as 80! ). If your simulation is going to be very CPU-intensive, you're probably better off subclassing either cMessage or rather cPacket, and adding the required parameters as ints, longs, bools, etc. to the new message class.
Some time in the future OMNeT++ will directly support message subclassing, and it will make the new parameters inspectable in the graphical user interface (Tkenv). This is a feature much demanded by users.
However, if you don't expect your simulations execute for hours, then cPar parameters are the most convenient way to go.
It is often necessary to encapsulate a message into another when you're modeling layered protocols of computer networks. Although you can encapsulate messages by adding them to the parameter list, there's a better way.
The encapsulate() function encapsulates a message into another one. The length of the message will grow by the length of the encapsulated message. An exception: when the encapsulating (outer) message has zero length, OMNeT++ assumes it is not a real packet but some out-of-band signal, so its length is left at zero.
cMessage *userdata = new cMessage("userdata");
userdata->setLength(8*2000);
cMessage *tcpseg = new cMessage("tcp");
tcpseg->setLength(8*24);
tcpseg->encapsulate(userdata);
ev << tcpseg->length() << endl; // --> 8*2024 = 16192
A message can only hold one encapsulated message at a time. The second encapsulate() call will result in an error. It is also an error if the message to be encapsulated isn't owned by the module..
You can get back the encapsulated message by decapsulate():
cMessage *userdata = tcpseg->decapsulate();
decapsulate() will decrease the length of the message accordingly, except if it was zero. If the length would become negative, an error occurs.
The encapsulatedMsg() function returns a pointer to the encapsulated message, or NULL if no message was encapsulated.
Readonly attributes
The following functions exist in cMessage:
bool isSelfMessage() cGate *senderGate(); // return NULL if scheduled cGate *arrivalGate(); // or unsent message int senderModuleId(); int senderGateId(); int arrivalModuleId(); int arrivalGateId(); simtime_t creationTime(); simtime_t sendingTime(); simtime_t arrivalTime(); bool arrivedOn(int g); bool arrivedOn(const char *s, int g=0);
TBD comments
Context pointer
cMessage contains a void* pointer which is set/returned by the setContextPointer() and contextPointer() functions:
void *context = ...; msg->setContextPointer( context ); void *context2 = msg->contextPointer();
It can be used for any purpose by the simulation programmer. It is not used by the simulation kernel, and it is treated as a mere pointer (no memory management is done on it).
Intended purpose: a module which schedules several self-messages (timers) will need to identify a self-message when it arrives back to the module, ie. the module will have to determine which timer went off and what to do then. The context pointer can be made to point at a data structure kept by the module which can carry enough "context" information about the event.
The cPacket class is derived from cMessage. It is intended as a base for all messages that model packets or frames in a telecommunications network.
cPacket adds two new data members to cMessage: protocol and PDU type (packet/frame/event type). Both are short integers, and are handled by the following member functions:
short protocol(); short pdu(); setProtocol(short p); setPdu(short p);
Acceptable message kind values are:
The cPacket constructor sets the message kind to MK_PACKET. Both MK_PACKET and MK_INFO are defined as negative integers. (Remember, negative message kind values are reserved for the simulation library.)
The protocol and PDU fields would ideally take a value from the protocol.h header in the simulation library. The contents of protocol.h is currently experimental; comments and contributions are welcome.
TDB examples for protocol and pdu values.
TBD include an example
Once the message has been created, it can be sent through an output gate using one of these functions:
send(cMessage *msg, const char *gate_name, int index); send(cMessage *msg, int gate);
For the first function, the argument gate_name is the name of the gate the message has to be sent through. If this gate is a vector gate, index determines though which particular output gate this has to be done; otherwise, the index argument is not needed.
The second function uses the gate number and because it does not have to search through the gate array, it is faster than the first one.
Examples:
send( new cMessage("token"), "out-gate");
send( new cMessage("token"), "vectorgate", i);
int out_gate_id = findGate("out-gate");
for (i=0; i<n; i++)
{
send( new cMessage("packet"), out_gate_id);
wait(in_time);
}
All message sending functions check that you actually own the message you are about to send. If the message is with another module, currently scheduled or in a queue etc., you'll get a runtime error. (The feature does not increase runtime overhead significantly, because it uses the object ownership management; it merely checks that the owner of the message is the module which wants to send it.)
It is often needed to model a delay (processing time etc) immediately followed by message sending. In OMNeT++, it is possible to implement it like this:
wait( some_delay ); send( msg, "outgate" );
If the module needs to react to messages that arrive during the delay, wait() cannot be used and the timer mechanism described under Implementing timers would need to be employed.
However, there is a more straightforward method than the above two, and this is delayed sending. Delayed sending can be done with one of these functions:
sendDelayed(cMessage *msg, double delay, const char *gate_name, int index); sendDelayed(cMessage *msg, double delay, int gate_id);
The arguments are the same as for send(), except for the extra delay parameter. The effect of the function is the same as if the module had kept the message for the delay interval and sent it afterwards. That is, the sending time of the message will be the current simulation time (time at the sendDelayed() call) plus the delay. The delay value must be nonnegative.
Example:
sendDelayed( new cMessage("token"), 0.005, "out-gate");
Sometimes it is necessary or convenient to ignore gates/connections and send a message directly to a remote destination module. The sendDirect() function does that, and it takes the pointer of the remote module (cModule *). You can also specify a delay and an input gate of the destination module.
cModule *destinationmodule = ...; double delay = truncnormal(0.005, 0.0001); sendDirect( new cMessage, delay, destinationmodule, "in" );
The destination module receives the message as if it was sent "normally".
With activity() only! The message receiving functions can only be used in the activity() function, handleMessage() gets the messages in its argument list.
A message can be received by a number of functions, the most general one is the receive() function:
cMessage *msg = receive();
Simple module objects contain a built-in queue object called putAsideQueue. The put-aside queue is used by some of the message-receiving functions.
There are two groups of functions that receive messages:
The functions receive()/receiveOn()check the put-aside queue first and try to return a message from it. Only if they do not find an appropriate message in the put-aside queue, will wait for new messages.
The functions receiveNew()/receiveNewOn() wait for new messages, ignoring the put-aside queue.
Furthermore, the ...On() functions expect messages to arrive on a specific gate. Messages that arrive on another gate are inserted the put-aside queue. The On-less versions accept any message.
Since the receive() and receiveOn() return messages also from the put-aside queue, the arrival times of messages they return can be less than the current simulation time. A naïve (and also incorrect) approach to check whether a message is a new one or it has been retrieved from the putaside-queue could be the following:
cMessage *msg = receive();
if (msg->arrivalTime()<simTime()) // not correct! several events may
// occur at the same simulation time
{
// handle msg as an old message
}
The correct way to do this is to check the putaside-queue:
bool queue_was_empty = putAsideQueue.empty();
cMessage *msg = receive();
if (!queue_was_empty)
{
// handle msg as an old message
}
To discard the contents of the put-aside queue, one could use the following code:
while (!putAsideQueue.empty()) delete receive();
To demonstrate receiveOn(), the following code fragment waits for a message on one specific input gate and discards all messages that arrived on other gates in the meanwhile:
cMessage *msg = receiveNewOn("important_input_gate");
while (!putAsideQueue.empty())
delete receive();
The above code is almost equivalent to the following, except that it preserves the previous contents of the put-aside queue:
cMessage *msg;
for(;;)
{
msg = receiveNew();
if (msg->arrivedOn("important_input_gate"))
break;
delete msg;
}
All message receiving functions can be given a timeout value. (This is a delta, not an absolute simulation time.) If an appropriate message doesn't arrive within the timeout period, the function returns a NULL pointer. An example:
simtime_t timeout = 3.0;
cMessage *msg = receive( timeout );
if (msg=NULL)
// timeout expired without any messages
else
// process message
With activity() only! The wait() function's implementation contains a receive() call which cannot be used in handleMessage().
The wait() function suspends the execution of the module for a given amount of simulation time (a delta), regardless whether messages arrive at the module in the meanwhile or not:
wait( delay_interval );
In other simulation software, wait() is often called hold. Internally, the wait() function is implemented by a scheduleAt() followed by a receive(). The wait() function is very convenient in modules that do not need to be prepared for arriving messages, for example message generators. An example:
for(;;)
{
wait( par("interarrival-time") );
// generate and send message
}
The messages that arrived during the wait() call will accumulate in the putaside-queue. The putaside-queue can be examined directly (an example was shown in the previous section), and its contents is also retrieved by the receive() or receiveOn() functions.
The module can send a message to itself using the scheduleAt() function:
scheduleAt( time, msg );
scheduleAt() accepts an absolute simulation time (usually simTime()+something). Messages sent via scheduleAt() are called self-messages, and in OMNeT++ they are used to model events which occur within the module. Self-messages are delivered to the module in the same way as other messages (via the usual receive calls or handleMessage()); the module may call the isSelfMessage() member of any received message to determine if it is a self-message.
Before self-messages are delivered, they can be cancelled (removed from the FES). This is particularly useful because self-messages are often used to model timers.
cancelEvent( msg );
The cancelEvent() function takes a pointer to the message to be cancelled, and also returns the same pointer. After having it cancelled, you may delete the message or reuse it in the next scheduleAt() calls. cancelEvent()gives an error if the message is not in the FES.
The following example shows how to implement timers:
cMessage *timeout_msg = new cMessage;
scheduleAt( simTime()+10.0, timeout_msg );
//...
cMessage *msg = receive();
if (msg == timeout_msg)
{
// timeout expired
}
else
{
// other message has arrived, timer can be cancelled now:
delete cancelEvent( timeout_msg );
}
You can determine if a message is currently in the FES by calling its isScheduled() member:
if (msg->isScheduled())
delete cancelEvent(msg);
else
...
An advanced version of the above code which also checks the put-aside queue:
if (msg->isScheduled())
delete cancelEvent(msg);
else if (putAsideQueue.contains(msg))
delete putAsideQueue.remove(msg);
else
...
You may have reasons to check whether a certain output gate is transmitting or to learn when it will finish transmission. This is done with gate object's isBusy() and transmissionFinishes() member functions. The latter function, transmissionFinishes() returns the time when the gate will finish its current transmission or (if it is currently free) when it finished its last transmission.
An example:
cMessage *packet = new cMessage("DATA");
packet->setLength( 1000 );
if (gate("TxGate")->isBusy()) // if gate is busy, wait until it
{ // becomes free
wait( gate("TxGate")->transmissionFinishes() - simTime());
}
send( packet, "TxGate");
If the connection with a data rate is not immediately the one connected to the simple module's output gate but the second one in the route, you have to check the second gate's busy condition. You would use the following code:
if (gate("mygate")->toGate()->isBusy())
//...
Note that if data rates change during the simulation, the changes will affect only the messages that are sent after the change.
Normal termination
You can finish the simulation with the endSimulation() function:
endSimulation();
However, typically you don't need endSimulation() because you can specify simulation time and CPU time limits in the ini file (see later).
Stopping on errors
If your simulation detects an error condition and wants to stop the simulation, you can do it with the error() member function of cModule. It is used like printf():
if (windowSize<1)
error("Invalid window size 0; must be >=1", windowSize);
Do not include a newline ("\n") or punctuation (period or exclamation mark) in the printed-out text, it will be added by OMNeT++.
Module parameters can be accessed with the par() member function of cModule:
cPar& delay_par = par("delay");
The cPar class is a general value-storing object. It supports type casts to numeric types, so parameter values can be read like this:
int num_tasks = par("num_tasks");
double proc_delay = par("proc_delay");
If the parameter is a random variable or its value can change during execution, it is best to store a reference to it and re-read the value each time it is needed:
cPar& wait_time = par("wait_time");
for(;;)
{
//...
wait( (simtime_t)wait_time );
}
If the wait_time parameter was given a random value (e.g. exponential(1.0)) in the NED source or the ini file, the above code results in a different delay each time.
Parameter values can also be changed from the program, during execution. If the parameter was taken by reference (with a ref modifier in the NED file), other modules will also see the change. Thus, parameters taken by reference can be used as a means of module communication.
An example:
par("wait_time") = 0.12;
Or:
cPar& wait_time = par("wait_time");
wait_time = 0.12;
See cPar explanation later in this manual for further information on how to change a cPar's value.
Gate objects
Module gates are cGate objects. Gate objects know whether and to which gate they are connected, and they can be asked about the parameters of the link (delay, data rate, etc.)
The gate()member function of cModule returns a pointer to a cGate object, and an overloaded form of the function lets you to access elements of a vector gate:
cGate *outgate = gate("out");
cGate *outvec5gate = gate("outvec",5);
For gate vectors, the first form returns the first gate in the vector (at index 0).
The isVector() member function can be used to determine if a gate belongs to a gate vector or not. But this is almost insignificant, because non-vector gates are treated as vectors with size 1.
Given a gate pointer, you can use the size() and index() member functions of cGate to determine the size of the gate vector and the index of the gate within the vector:
int size2 = outvec5gate->size(); // --> size of outvec[] int index = outvec5gate->index(); // --> 5 (it is gate 5 in the vector)
For non-vector gates, size() returns 1 and index() returns 0.
The type() member function returns a character, 'I' for input gates and 'O' for output gates:
char type = outgate->type() // --> 'O'
Gate IDs
Module gates (input and output, single and vector) are stored in an array within their modules. The gate's position in the array is called the gate ID. The gate ID is returned by the id() member function:
int id = outgate->id();
For a module with input gates from_app and in[3] and output gates of to_app and status, the array may look like this:
The array may have empty slots. Gate vectors are guaranteed to occupy contiguous IDs, that is, it is legal to calculate the ID of gate[k] as gate("gate",0).id()+k.
Message sending and receiving functions accept both gate names and gate IDs; the functions using gates IDs are a bit faster. Gate IDs do not change during execution, so it is often worth retrieving them in advance and using them instead of gate names.
Gate IDs can also be determined with the findGate() member of cModule:
int id1 = findGate("out");
int id2 = findGate("outvect",5);
Link parameters
The following member functions return the link attributes:
cLinkType *link = outgate->link(); cPar *d = outgate->delay(); cPar *e = outgate->error(); cPar *r = outgate->datarate();
Transmission state
The isBusy() member function returns whether the gate is currently transmitting, and if so, the transmissionFinishes() member function returns when it will finish transmitting.
Connectivity
TBD figure
The isConnected() member function returns whether the gate is connected. If the gate is an output gate, the gate to which it is connected is obtained by the toGate() member function. For input gates, the function is fromGate().
cGate *gate = gate("somegate");
if (gate->isConnected())
{
cGate *othergate = (gate->type()=='O') ?
gate->toGate() : gate->fromGate();
ev << "gate is connected to: " << othergate->fullPath() << endl;
}
else
{
ev << "gate not connected" << endl;
}
An alternative to isConnected() is to check the return value of toGate() or fromGate(). The following code is fully equivalent to the one above:
cGate *gate = gate("somegate");
cGate *othergate = (gate->type()=='O') ?
gate->toGate() : gate->fromGate();
if (othergate)
ev << "gate is connected to: " << othergate->fullPath() << endl;
else
ev << "gate not connected" << endl;
To find out to which simple module a given output gate leads finally, you would have to walk along the path like this (the ownerModule() member function returns the module to which the gate belongs):
cGate *gate = gate("out");
while (gate->toGate()!=NULL)
{
gate = gate->toGate();
}
cModule *destmod = gate->ownerModule();
but luckily, there are two convenience functions which do that: sourceGate() and destinationGate().
Module vectors
If a module is part of a module vector, the index() and size() member functions can be used to query its index and the vector size:
ev << "This is module [" << module->index() <<
"] in a vector of size [" << module->size() << "].\n";
Module IDs
Each module in the network has a unique ID that is returned by the id() member function. The module ID is used internally by the simulation kernel to identify modules.
int myModuleId = id();
If you know the module ID, you can ask the simulation object (a global variable) to get back the module pointer:
int id = 100; cModule *mod = simulation.module( id );
Module IDs are guaranteed to be unique, even when modules are created and destroyed dynamically. That is, an ID which once belonged to a module which was deleted is never issued to another module later.
Walking up and down the module hierarchy
The surrounding compound module can be accessed by the parentModule() member function:
cModule *parent = parentModule();
For example, the parameters of the parent module are accessed like this:
double timeout = parentModule()->par( "timeout" );
cModule's findSubmodule() and submodule() member functions make it possible to look up the module's submodules by name (or name+index if the submodule is in a module vector). The first one returns the numeric module ID of the submodule, and the latter returns the module pointer. If the submodule is not found, they return -1 or NULL, respectively. (NEW)
int submodID = compoundmod->findSubmodule("child",5);
cModule *submod = compoundmod->submodule("child",5);
The moduleByRelativePath() member function can be used to find a submodule nested deeper than one level below. For example,
compoundmod->moduleByRelativePath("child[5].grandchild");
would give the same results as
compoundmod->submodule("child",5)->submodule("grandchild");
(Provided that child[5] does exist, because otherwise the second version will crash with an access violation because of the NULL pointer.)
The cSimulation::moduleByPath() function is similar to cModule's moduleByRelativePath() function, and it starts the search at the top-level module.
Iterating over submodules
To access all modules within a compound module, use cSubModIterator. For example:
for (cSubModIterator submod(*parentModule()); !submod.end(); submod++)
{
ev << submod()->fullName();
}
(submod() is pointer to the current module the iterator is at.)
The above method can also be used to iterate along a module vector, since the name() function returns the same for all modules:
for (cSubModIterator submod(*parentModule()); !submod.end(); submod++)
{
if (submod()->isName( name() )) // if submod() is in the same
// vector as this module
{
int its_index = submod()->index();
// do something to it
}
}
Walking along links
To determine the module at the other end of a connection, use cGate's fromGate(), toGate() and ownerModule() functions. For example:
cModule *neighbour = gate( "outputgate" )->toGate()->ownerModule();
For input gates, you would use fromGate() instead of toGate().
Why
If you do not know how many modules you'll need, you can create modules dynamically and dispose of them when they are no longer needed. Both simple and compound modules can be created this way. If you create a compound module dynamically, all its submodules will be recursively built.
Let's suppose you are implementing a transport protocol for a computer network model. It is convenient to have a separate module to handle each connection. However, there's no way to know how many connections there'll be simultaneously. The solution is to create a manager module which receives connection requests and creates a module for each connection. The Dyna example simulation does something like this.
It is often convenient to use direct message sending with dynamically created modules.
Module factories
TBD
cModuleType *moduleType = findModuleType("TCPConnectionHandler");
Simple form
Mainly for creating simple modules.
TBD
cModuleType has createScheduleInit(const char *name, cModule *parentmod) convenience function to get a module up and running in one step.
mod = modtype->createScheduleInit("name",this);
Does create()+buildInside()+callInitialize()+scheduleStart(now).
Should work for both simple and compound modules.
Not applicable if the module:
Example:
TBD
Expanded form
If the previous simple form cannot be used. There are 5 steps:
Step 1. find descriptor object
Step 2. create module
Step 3. set up parameters and gate sizes (if needed)
Step 4. call function that builds out submodules and finalizes the module
Step 5. call function that creates activation message(s) for the new simple module(s)
Each step (except for Step 3.) can be done with one line of code.
See the following example where Step 3. is omitted:
// find descriptor object
cModuleType *moduleType = findModuleType("TCPConnectionHandler");
// create (possibly compound) module and build its submodules (if any)
cModule *module = moduleType->create( "TCPconn", this );
moduleType->buildInside( module );
// create activation message
module->scheduleStart( simTime() );
If you want to set up parameter values or gate vector sizes (Step 3.), the code goes between the create() and buildInside() calls:
cModuleType *moduleType = findModuleType("TCP-conn-handler");
cModule *module = moduleType->create( "TCPconn", this );
// set up parameters and gate sizes before we set up its submodules
module->par("window-size") = 4096;
module->setGateSize("to-apps", 3);
moduleType->buildInside( module );
module->scheduleStart( simTime() );
To delete a module dynamically:
module->deleteModule();
If the module was a compound module, this involves recursively destroying all its submodules. A simple module can also delete itself; in this case, if the module was implemented using activity(), the deleteModule() call does not return to the caller (the reason is that deleting the module also deletes the CPU stack of the coroutine).
Currently, you cannot safely delete a compound module from a simple module in it; you must delegate the job to a module outside the compound module.
Creating connections
There are two functions that you can use to connect gates. For a normal user, they are useful for creating connections to dynamically created modules.
connect( cModule *src_module, int src_gatenumber,
cLinkType *channeltype,
cModule *dest_module, int dest_gatenumber );
connect( cModule *src_module, int src_gatenumber,
cPar *delay, cPar *error, cPar *datarate,
cModule *dest_module, int dest_gatenumber );
Any of the channeltype, delay, error and datarate pointers can be NULL.
An example:
connect( this, findGate("out"),
(cLinkType *)NULL,
module, module->findGate("in",0)
);
The cTopology class was designed primarily to support routing in telecommunication or multiprocessor networks.
A cTopology object stores an abstract representation of the network in graph form:
You can specify which modules (either simple or compound) you want to include in the graph. The graph will include all connections among the selected modules. In the graph, all nodes are at the same level, there's no submodule nesting. Connections which span across compound module boundaries are also represented as one graph edge. Graph edges are directed, just as module gates are.
If you're writing a router or switch model, the cTopology graph can help you determine what nodes are available through which gate and also to find optimal routes. The cTopology object can calculate shortest paths between nodes for you.
The mapping between the graph (nodes, edges) and network model (modules, gates, connections) is preserved: you can easily find the corresponding module for a cTopology node and vica versa.
You can extract the network topology into a cTopology object by a single function call. You have several ways to select which modules you want to include in the topology:
First, you can specify which node types you want to include. The following code extracts all modules of type Router or User. (Router and User can be both simple and compound module types.)
cTopology topo; topo.extractByModuleType( "Router", "User", NULL );
Any number of module types (up to 32) can be supplied; the list must be terminated by NULL.
Second, you can extract all modules which have a certain parameter:
topo.extractByParameter( "ip_address" );
You can also specify that the parameter must have a certain value for the module to be included in the graph:
cPar yes = "yes"; topo.extractByParameter( "include_in_topo", &yes );
The third form allows you to pass a function which can determine for each module whether it should or should not be included. You can have cTopology pass supplemental data to the function through a void* pointer. An example which selects all top-level modules (and does not use the void* pointer):
int select_function(cModule *mod, void *)
{
return mod->parentModule() == simulation.systemModule();
}
topo.extractFromNetwork( select_function, NULL );
TBD one more example which does use the void* ptr.
A cTopology object uses two types: sTopoNode for nodes and sTopoLink for edges. (sTopoLinkIn and sTopoLinkOut are 'aliases' for sTopoLink; we'll speak about them later.)
Once you have the topology extracted, you can start exploring it. Consider the following code (we'll explain it shortly):
for (int i=0; i<topo.nodes(); i++)
{
sTopoNode *node = topo.node(i);
ev << "Node i=" << i << " is " << node->module()->fullPath() << endl;
ev << " It has " << node->outLinks() << " conns to other nodes\n";
ev << " and " << node->inLinks() << " conns from other nodes\n";
ev << " Connections to other modules are:\n";
for (int j=0; j<node->outLinks(); j++)
{
sTopoNode *neighbour = node->out(j)->remoteNode();
cGate *gate = node->out(j)->localGate();
ev << " " << neighbour->module()->fullPath() <<
" through gate " << gate->fullName() << endl;
}
}
The nodes() member function (1st line) returns the number of nodes in the graph, and node(i) returns a pointer to the ith node, an sTopoNode structure.
The correspondence between a graph node and a module can be obtained by:
sTopoNode *node = topo.nodeFor( module ); cModule *module = node->module();
The nodeFor() member function returns a pointer to the graph node for a given module. (If the module is not in the graph, it returns NULL). nodeFor() uses binary search within the cTopology object so it is fast enough.
sTopoNode's other member functions let you determine the connections of this node: inLinks(), outLinks() return the number of connections, in(i) and out(i) return pointers to graph edge objects.
By calling member functions of the graph edge object, you can determine the modules and gates involved. The remoteNode() function returns the other end of the connection, and localGate(), remoteGate(), localGateId() and remoteGateId() return the gate pointers and ids of the gates involved. (Actually, the implementation is a bit tricky here: the same graph edge object sTopoLink is returned either as sTopoLinkIn or as sTopoLinkOut so that "remote" and "local" can be correctly interpreted for edges of both directions.)
The real power of cTopology is in finding shortest paths in the network to support optimal routing. cTopology finds shortest paths from all nodes to a target node. The algorithm is computationally inexpensive. In the simplest case, all edges are assumed to have the same weight.
A real-life example when we have the target module pointer, finding the shortest path looks like this:
sTopoNode *targetnode = topo.nodeFor( targetmodule ); topo.unweightedSingleShortestPathsTo( targetnode );
This performs the Dijkstra algorithm and stores the result in the cTopology object. The result can then be extracted using cTopology and sTopoNode methods. Naturally, each call to unweightedSingleShortestPathsTo() overwrites the results of the previous call.
Walking along the path from our module to the target node:
sTopoNode *node = topo.nodeFor( this );
if (node == NULL)
{
ev << "We (" << fullPath() << ") are not included in the topology.\n";
}
else if (node->paths()==0)
{
ev << "No path to destination.\n";
}
else
{
while (node != topo.targetNode())
{
ev << "We are in " << node->module()->fullPath() << endl;
ev << node->distanceToTarget() << " hops to go\n";
ev << "There are "<< node->paths() <<
" equally good directions, taking the first one\n";
sTopoLinkOut *path = node->path(0);
ev << "Taking gate " << path->localGate()->fullName() <<
" we arrive in " << path->remoteNode()->module()->fullPath() <<
" on its gate " << path->remoteGate()->fullName() << endl;
node = path->remoteNode();
}
}
The purpose of the distanceToTarget() member function of a node is self-explanatory. In the unweighted case, it returns the number of hops. The paths() member function returns the number of edges which are part of a shortest path, and path(i) returns the ith edge of them as sTopoLinkOut. If the shortest paths were created by the ...SingleShortestPaths() function, paths() will always return 1 (or 0 if the target is not reachable), that is, only one of the several possible shortest paths are found. The ...MultiShortestPathsTo() functions find all paths, at increased run-time cost. The cTopology's targetNode() function returns the target node of the last shortest path search.
You can enable/disable nodes or edges in the graph. This is done by calling their enable() or disable() member functions. Disabled nodes or edges are ignored by the shortest paths calculation algorithm. The enabled() member function returns the state of a node or edge in the topology graph.
One usage of disable() is when you want to determine in how many hops the target node can be reached from our node through a particular output gate. To calculate this, you calculate the shortest paths to the target from the neighbor node, but you must disable the current node to prevent the shortest paths from going through it:
sTopoNode *thisnode = topo.nodeFor( this );
thisnode->disable();
topo.unweightedSingleShortestPathsTo( targetnode );
thisnode->enable();
for (int j=0; j<thisnode->outLinks(); j++)
{
sTopoLinkOut *link = thisnode->out(i);
ev << "Through gate " << link->localGate()->fullName() << " : " <<
1 + link->remoteNode()->distanceToTarget() << " hops" << endl;
}
In the future, other shortest path algorithms will also be implemented:
unweightedMultiShortestPathsTo(sTopoNode *target); weightedSingleShortestPathsTo(sTopoNode *target); weightedMultiShortestPathsTo(sTopoNode *target);
Random number generation is considered to be an important issue. The random number generator used in OMNeT++ is a linear congruential generator (LCG) with a cycle length of 231-2. The startup code of OMNeT++ contains code that checks if the random number generator works OK, so you do not have to worry about this if you port the simulator to a new architecture or use a different compiler.
If a simulation program uses random numbers for more than one purpose, the numbers should come from different random number generators. OMNeT++ provides several independent random number generators (by default 32; this number can be changed in defs.h).
To avoid unwanted correlation, it is also important that different simulation runs and different random number sources within one simulation run use non-overlapping series of random numbers, so the generators should be started with seeds well apart. For selecting good seeds, the seedtool program can be used (it is documented later).
The random number generator was taken from [JAIN91, pp. 441-444,455]. It has the following properties:
The concrete implementation:
long intrand()
{
const long int a=16807, q=127773, r=2836;
seed=a*(seedq) - r*(seed/q);
if (seed<=0) seed+=INTRAND_MAX;
return seed;
}
The generator is directly accessible through the intrand() function:
long rnd = intrand(); // in the range 1..INTRAND_MAX-1
The random number seed can be specified in the ini file (random-seed=) or set directly from within simple modules with the randseed() function:
randseed( 10 ); // set seed to 10 long seed = randseed(); // current seed value
Zero is not allowed as a seed.
The intrand() and randseed() functions use generator 0. They have another variant which uses a specified generator:
long rnd = genk_intrand(6); // like intrand(), using generator 6 genk_randseed( k, 167 ); // set seed of generator k to 167
The intrand(n) and dblrand() functions are based on intrand():
int dice = 1 + intrand(6); // result of intrand(6) is in the range 0..5
// (it is calculated as intrand())
double prob = dblrand(); // in the range 0.0..1.0
// calculated as intrand()/(double)INTRAND_MAX
They also have their counterparts that use generator k:
int dice = 1 + genk_intrand(k,6); // uses generator k double prob = genk_dblrand(k); // ""
The following functions are based on dblrand() and return random variables of different distributions:
double uniform(double lower_limit, double upper_limit); double intuniform(double lower_limit, double upper_limit); double exponential(double mean); double normal(double mean, double deviation); double truncnormal(double mean, double deviation);
They are the same functions that can be used in NED files. intuniform() generates integers including both the lower and upper limit, so for example the outcome of tossing a coin could be written as intuniform(1,2). truncnormal() is the normal distribution truncated to nonnegative values; its implementation generates a number with normal distribution and if the result is negative, it keeps generating other numbers until the outcome is nonnegative.
The counterparts of the above functions using generator k:
double genk_uniform(double k, double lower_limit, double upper_limit); double genk_intuniform(double k, double lower_limit, double upper_limit); double genk_exponential(double k, double mean); double genk_normal(double k, double mean, double deviation); double genk_truncnormal(double k, double mean, double deviation);
Note that they take the number of the generator as a double; it is so because these functions are designed so that they can be used with the cPar class and in NED files. You will find more information about this in the section describing cPar.
If the above distributions do not suffice, you can write your own functions. If you register your functions with the Register_Function() macro, you can use them in NED files and ini files too. You can find the implementation of many distributions in the class library of GNU C++.
You can also specify your distribution as a histogram. The cLongHistogram, cDoubleHistogram, cVarHistogram, cKSplit or cPSquare classes are there to generate random numbers from equidistant-cell or equiprobable-cell histograms. This feature is documented later, with the statistical classes.
Basic usage
cQueue is a container class that acts as a queue. cQueue can hold objects of type derived from cObject (almost all classes from the OMNeT++ library), such as cMessage, cPar, etc. Internally, cQueue uses a double-linked list to store the elements.
As an example of use, the simple modules' put-aside queues (putAsideQueue member) are cQueues which store cMessage objects. (However, the Future Event Set [FES] is not a cQueue; it is implemented with heap [class cMessageHeap] because it is a lot more efficient.)
A queue object has a head and a tail. Normally, new elements are inserted at its head and elements are removed at its tail.
The basic cQueue member functions dealing with insertion and removal are insert() and pop(). They are used like this:
cQueue queue("my-queue");
cMessage *msg;
// insert messages
for (int i=0; i<10; i++)
{
msg = new cMessage;
queue.insert( msg );
}
// remove messages
while( ! queue.empty() )
{
msg = (cMessage *)queue.pop();
delete msg;
}
The length() member function returns the number of items in the queue, and empty() tells whether there's anything in the queue.
There are other functions dealing with insertion and removal. The insertBefore() and insertAfter() functions insert a new item exactly before and after a specified one, regardless of the ordering function.
The tail() and head() functions return pointers to the objects at the tail and head of the queue, without affecting queue contents.
The pop()function can be used to remove items from the tail of the queue, and the remove() function can be used to remove any item known by its pointer from the queue:
queue.remove( msg );
Priority queue
By default, cQueue implements a FIFO, but it can also act as a priority queue, that is, it can keep the inserted objects ordered. If you want to use this feature, you have to provide a function that takes two cObject pointers, compares the two objects and returns -1, 0 or 1 as the result (see the reference for details). An example of setting up an ordered cQueue:
cQueue sortedqueue("sortedqueue", cObject::cmpbyname, true );
// sorted by object name, ascending
If the queue object is set up as an ordered queue, the insert() function uses the ordering function: it searches the queue contents from the head until it reaches the position where the new item needs to be inserted, and inserts it there.
Iterators
Normally, you can only access the objects at the head or tail of the queue. However, if you use an iterator class, cQueueIterator, you can examine each object in the queue.
The cQueueIterator constructor takes two arguments, the first is the queue object and the second one specifies the initial position of the iterator: 0=tail, 1=head. Otherwise it acts as any other OMNeT++ iterator class: you can use the ++ and -- operators to advance it, the () operator to get a pointer to the current item, and the end() member function to examine if you're at the end (or the beginning) of the queue.
An example:
for( cQueueIterator iter(queue,1); !iter.end(), iter++)
{
cMessage *msg = (cMessage *) iter();
//...
}
Basic usage
cArray is a container class that holds objects derived from cObject. cArray stores the pointers of the objects inserted instead of making copies. cArray works as an array, but if it gets full, it grows automatically. Internally, cArray is implemented with an array of pointers; if the array gets full, it is reallocated.
cArray objects are used in OMNeT++ to store parameters attached to messages, and internally, for storing module parameters and gates.
Creating an array:
cArray array("array");
Adding an object at the first free index:
cPar *p = new cPar("par");
int index = array.add( p );
Adding an object at a given index (if the index is occupied, you'll get an error message):
cPar *p = new cPar("par");
int index = array.addAt(5,p);
Finding an object in the array:
int index = array.find(p);
Getting a pointer to an object at a given index:
cPar *p = (cPar *) array[index];
You can also search the array or get a pointer to an object by the object's name:
int index = array.find("par");
Par *p = (cPar *) array["par"];
You can remove an object from the array by calling remove() with the object name, the index position or the object pointer:
array.remove("par");
array.remove(index);
array.remove( p );
The remove() function doesn't deallocate the object, but it returns the object pointer. If you also want to deallocate it, you can write:
delete array.remove( index );
Iteration
cArray has no iterator, but it's easy to loop through all the indices with an integer variable. The items() member function returns the largest index plus one.
for (int i=0; i<array.items(); i++)
{
if (array[i]) // is this position used?
{
cObject *obj = array[i];
ev << obj->name() << endl;
}
}
There are two container classes to store non-object items: cLinkedList and cBag. The first one parallels with cQueue, the second one with cArray. They can be useful if you have to deal with C structs or objects that are not derived from cObject.
See the class library reference for more info about them.
cPar is a class that was designed to hold a value. The value is numeric (long or double) in the first place, but string, pointer and other types are also supported.
cPar is used in OMNeT++ in the following places:
There are many ways to set a cPar's value. One is the set...Value() member functions:
cPar pp("pp");
pp.setDoubleValue(1.0);
or by using overloaded operators:
cPar pp("pp");
pp = 1.0;
For reading its value, it is best to use overloaded type cast operators:
double d1 = (double)pp; // or simply: double d2 = pp;
Long integers:
pp = 89363L; // or: pp.setLongValue( 89363L );
Character string:
pp = "hi there"; // or: pp.setStringValue( "hi there" );
The cPar object makes its own copy of the string, so the original one does not need to be preserved. Short strings (less than ~20 chars) are handled more efficiently because they are stored in the object's memory space (and are not dynamically allocated).
There are several other types cPar can store: such as boolean, void* pointer; cObject* pointer, function with constant args; they will be mentioned in the next section.
For numeric and string types, an input flag can be set. In this case, when the object's value is first used, the parameter value will be searched for in the configuration (ini) file; if it is not found there, the user will be given a chance to enter the value interactively.
Examples:
cPar inp("inp");
inp.setPrompt("Enter my value:");
inp.setInput( true ); // make it an input parameter
double a = (double)inp; // the user will be prompted HERE
Setting cPar to call a function with constant arguments can be used to make cPar return random variables of different distributions:
cPar rnd("rnd");
rnd.setDoubleValue(intuniform, -10.0, 10.0);// uniform distr.
rnd.setDoubleValue(normal, 100.0, 5.0); // normal distr. (mean,dev)
rnd.setDoubleValue(exponential, 10.0); // exponential distr. (mean)
intuniform, normal etc. are ordinary C functions taking double arguments and returning double. Each time you read the value of a cPar containing a function like above, the function will be called with the given constant arguments (e.g. normal(100.0,5.0)) and its return value used.
The above functions use number 0 from the several random number generators. To use another generator, use the genk_xxx versions of the random functions:
rnd.setDoubleValue(genk_normal, 3, 100.0, 5.0); // uses generator 3
A cPar object can also be set to return a random variable from a distribution collected by a statistical data collection object:
cDoubleHistogram hist = ....; // the distribution
cPar rnd2("rnd2");
rnd2.setDoubleValue(hist);
cPar can store pointers to OMNeT++ objects. You can use both assignment and the setObjectValue() member function:
cQueue *queue = new cQueue("queue"); // just an example
cPar par1, par2;
par1 = (cObject *) queue;
par2.setObjectValue( queue );
To get the store pointer back, you can use typecast or the objectValue() member function:
cQueue *q1 = (cQueue *)(cObject *)par1; cQueue *q2 = (cQueue *)par2.objectValue();
Whether the cPar object will own the other object or not is controlled by the takeOwnership() member function, just as with container classes. This is documented in detail in the class library reference. By default, cPar will own the object.
cPar can be used to store non-object pointers (for example C structs) or non-OMNeT++ object types in the parameter object. It works very similarly to the above mechanism. An example:
double *mem = new double[15]; cPar par1, par2; par1 = (void *) mem; par2.setPointerValue( (void *)mem ); ... double *m1 = (double *)(void *)par1; double *m2 = (double *)par2.pointerValue();
Memory management can be specified by cPar's configPointer() member function. It takes three arguments: a pointer to a user-supplied deallocation function, a pointer to a user-supplied duplication function and an item size. If all three are 0 (NULL), no memory management is done, that is, the pointer is treated as a mere pointer. This is the default behaviour. If you supply only the item size (and both function pointers are NULL), cPar will use the delete operator to deallocate the memory area when the cPar object is destructed, and it will use new char[size] followed by a memcpy() to duplicate the memory area whenever the cPar object is duplicated. If you need more sophisticated memory management, you can supply your own deallocation and duplication functions. All this is documented in detail in the class library reference. An example for simple memory management:
double *mem = new double[15]; cPar par; par.setPointerValue((void *) mem); par.configPointer(NULL, NULL, 15*sizeof(double)); // -> now if par goes out of scope, it will delete the 15-double array.
The configPointer() setting only affects what happens when the cPar is deleted, duplicated or copied, but does not apply to assigning new pointers. That is, if you assign a new void* to the cPar, you simply overwrite the pointer - the block denoted by the old pointer is not deleted. This fact can be used to extract some dynamically allocated block out of the cPar: carrying on the previous example, you would extract the array of 15 doubles from the cPar like this:
double *mem2 = (double *)par.pointerValue(); par.setValue( (void *)0 ); // -> now par has nothing to do with the double[15] array
However, if you assign some non-pointer value to the cPar, beware: this will activate the memory management for the block. If you temporarily use the same cPar object to store other than void* ('P') values, the configPointer() setting is lost.
This feature is rarely needed by the user, it is more used internally. A cPar object can also store expressions. In this case, the expression must be given in reversed Polish form. An example:
sXElem *expression = new sXElem[5];
expression[0] = &par( "count" ); // pointer to module parameter
expression[1] = 1;
expression[2] = '+';
expression[3] = 2;
expression[4] = '/';
cPar expr("expr");
expr.setDoubleValue(expression,5);
The cPar object created above contains the (count+1)/2 expression where count is a module parameter. Each time the cPar is evaluated, it recalculates the expression, using the current value of count. Note the & sign in front of par("count") expression: if it was not there, the parameter would be taken by value, evaluated once and then the resulting constant would be used.
Another example is a distribution with mean and standard deviation given by module parameters:
sXElem *expression = new sXElem[3];
expression[0] = &par("mean" );
expression[1] = &par("stddev");
expression[2] = normal; // pointer to the normal(double,double) func.
cPar expr("expr");
expr.setDoubleValue(expression,3);
For more information, see the reference and the code NEDC generates for parameter expressions.
A cPar object can be set to stand for a value actually stored in another cPar object. This is called indirect or redirected value. When using redirection, every operation on the value (i.e. reading or changing it) will be actually done to the other cPar object:
Redirection is how module parameters taken by reference are implemented. The redirection does not include name strings. That is, if you say A->setName("newname") in the above example, A's name will be changed as the name member is not redirected. (This is natural if you consider parameters taken by reference: a parameter should/can have different name than the value it refers to.)
You create a redirection with the setRedirection() function:
cPar *bb = new cPar("bb"); // background value
bb = 10L;
cPar a("a"); // we'll redirect this object
a.setRedirection(bb); // create redirection
Now every operation you do on a's value will be done to bb:
long x = a; // returns bb's value, 10L a = 5; // bb's value changes to 5
The only way to determine whether a is really holding the value or it is redirected to another cPar is to use the isRedirected() member function which returns a bool, or redirection() which returns the pointer to the background object, or NULL if there's no redirection:
cPar *redir = a.redirection(); // returns bb's pointer
if (redir != NULL)
ev << "a is redirected to " << redir->name() << endl;
To break the link between the two objects, use the cancelRedirection() member function. (No other method will work, including assigning a the value of another cPar object.) The cancelRedirection() function gives the (long)0 value to the redirected object (the other will be unaffected). If you want to cancel the indirection but keep the old value, you can do something like this:
cPar *value = a.redirection(); // bb's pointer a.cancelRedirection(); // break the link; value of a is now 0 a = *value; // copy the contents of bb into a
Internally, cPar objects identify the types of the stored values by type characters. The type character is returned by the type() member function:
cPar par = 10L; char typechar = par.type(); // returns 'L'
The full table of type characters is presented in the Summary section below.
TBD isNumeric() function.
The various cPar types and the member functions used to manipulate them are summarized in the following table:
| Type char | Type name | Member functions | Description |
| S | string |
setStringValue( const char *); const char * stringValue(); op const char *(); op=(const char *); | string value. Short strings (len<=27) are stored inside cPar object, without using heap allocation. |
| B | boolean | setBoolValue(bool); bool boolValue(); op bool(); op=(bool); | boolean value. Can also be retrieved from the object as long (0 or 1). |
| L | long int | setLongValue(long); long longValue(); op long(); op=(long); | signed long integer value. Can also be retrieved from the object as double. |
| D | double | setDoubleValue(double); double doubleValue(); op double(); op=(double); | double-precision floating point value. |
| F | function | setDoubleValue( MathFunc, [double], [double], [double]); double doubleValue(); op double(); | Mathematical function with constant arguments. The function is given by its pointer; it must take 0,1,2 or 3 doubles and return a double. This type is mainly used to generate random numbers: e.g. the function takes mean and standard deviation and returns random variable of a certain distribution. |
| X | expr. | setDoubleValue( sXElem*,int); double doubleValue(); op double(); | Reverse Polish expression. Expression can contain constants, cPar objects, refer to other cPars (e.g. module parameters), can use many math operators (+-*/^ 0.000000e+000tc), function calls (function must take 0,1,2 or 3 doubles and return a double). The expression must be given is in an sXElem array (see later). |
| T | distrib. | setDoubleValue( cStatistic*); double doubleValue(); op double(); | random variable generated from a distribution collected by a statistical data collection object (derived from cStatistic). |
| P | void* pointer | setPointerValue(void*); void *pointerValue(); op void *(); op=(void *); | pointer to a non-cObject item (C struct, non-cObject object etc.) Memory management can be controlled through the configPointer() member function. |
| O | object pointer | setObjectValue(cObject*); cObject *objectValue(); op cObject *(); op=(cObject *); | pointer to an object derived from cObject. Ownership management is done through takeOwnership(). |
| I | indirect value | setRedirection(cPar*); bool isRedirected(); cPar *redirection(); cancelRedirection(); | value is redirected to another cPar object. All value setting and reading operates on the other cPar; even the type() function will return the type in the other cPar (so you'll never get 'I' as the type). This redirection can only be broken with the cancelRedirection() member function. Module parameters taken by REF use this mechanism. |
There are several statistic and result collection classes: cStdDev, cWeightedStdDev, LongHistogram, cDoubleHistogram, cVarHistogram, cPSquare and cKSplit. They are all derived from the abstract base class cStatistic.
Basic usage
One can insert an observation into a statistic object with the collect() function or the += operator (they are equivalent). cStdDev has the following methods for getting statistics out of the object: samples(), min(), max(), mean(), stddev(), variance(), sum(), sqrSum() with the obvious meanings. An example usage for cStdDev:
cStdDev stat("stat");
for (int i=0; i<10; i++)
stat.collect( normal(0,1) );
long num_samples = stat.samples();
double smallest = stat.min(),
largest = stat.max();
double mean = stat.mean(),
standard_deviation = stat.stddev(),
variance = stat.variance();
Initialization and usage
The distribution estimation classes (the histogram classes, cPSquare and cKSplit) are derived from cDensityEstBase. Distribution estimation classes (except for cPSquare) assume that the observations are within a range. You may specify the range explicitly (based on some a-priori info about the distribution) or you may let the object collect the first few observations and determine the range from them. Methods which let you specify range settings are part of cDensityEstBase. The following member functions exist:
setRange(lower,upper); setRangeAuto(num_firstvals, range_ext_factor); setRangeAutoLower(upper, num_firstvals, range_ext_factor); setRangeAutoUpper(lower, num_firstvals, range_ext_factor); setNumFirstVals(num_firstvals);
The following example creates a histogram with 20 cells and automatic range estimation:
cDoubleHistogram histogram("histogram", 20);
histogram.setRangeAuto(100,1.5);
Here, 20 is the number of cells (not including the underflow/overflow cells, see later), and 100 is the number of observations to be collected before setting up the cells. 1.5 is the range extension factor. It means that the actual range of the initial observations will be expanded 1.5 times and this expanded range will be used to lay out the cells. This method increases the chance that further observations fall in one of the cells and not outside the histogram range.
After the cells have been set up, collecting can go on.
The transformed() function returns true when the cells have already been set up. You can force range estimation and setting up the cells by calling the transform() function.
The observations that fall outside the histogram range will be counted as underflows and overflows. The number of underflows and overflows are returned by the underflowCell() and overflowCell() member functions.
You create a P2 object by specifying the number of cells:
cPSquare psquare("interarrival-times", 20);
Afterwards, a cPSquare can be used with the same member functions as a histogram.
There are three member functions to explicitly return cell boundaries and the number of observations is each cell. cells() returns the number of cells, basepoint(int k) returns the kth base point, cell(int k) returns the number of observations in cell k, and cellPDF(int k) returns the PDF value in the cell. These functions work for all histogram types, cPSquare and cKSplit.
An example:
long n = histogram.samples();
for (int i=0; i<histogram.cells(); i++)
{
double cellWidth = histogram.basepoint(i+1)-histogram.basepoint(i);
int count = histogram.cell(i);
double pdf = histogram.cellPDF(i);
//...
}
The pdf(x) and cdf(x) member functions return the value of the probability density function and the cumulated density function at a given x, respectively.
The random() member function generates random numbers from the distribution stored by the object:
double rnd = histogram.random();
cStdDev assumes normal distribution.
You can also wrap the distribution object in a cPar:
cPar rnd_par("rnd_par");
rnd_par.setDoubleValue(&histogram);
The cPar object stores the pointer to the histogram (or P2 object), and whenever it is asked for the value, calls the histogram object's random() function:
double rnd = (double)rnd_par; // random number from the cPSquare
Storing/loading distributions
The statistic classes have loadFromFile() member functions that read the histogram data from a text file. If you need a custom distribution that cannot be written (or it is inefficient) as a C function, you can describe it in histogram form stored in a text file, and use a histogram object with loadFromFile().
You can also use saveToFile()that writes out the distribution collected by the histogram object:
FILE *f = fopen("histogram.dat","w");
histogram.saveToFile( f ); // save the distribution
fclose( f );
FILE *f2 = fopen("histogram.dat","r");
cDoubleHistogram hist2("Hist-from-file");
hist2.loadFromFile( f2 ); // load stored distribution
fclose( f2 );
Histogram with custom cells
cVarHistogram class. TBD comments.
Now we do support the following 2 uses of cVarHistogram:
Transform types for cVarHistogram:
Creating an object:
cVarHistogram(const char *s=NULL,
int numcells=11,
int transformtype=HIST_TR_AUTO_EPC_DBL);
Manually adding a cell boundary:
void addBinBound(double x);
Rangemin and rangemax is chosen after collecting the num_firstvals initial observations. One cannot add cell boundaries when histogram is already transformed.
Purpose
The k-split algorithm is an on-line distribution estimation method. It was designed for on-line result collection in simulation programs. The method was proposed by Varga and Fakhamzadeh in 1997. The primary advantage of k-split is that without having to store the observations, it gives a good estimate without requiring a-priori information about the distribution, including the sample size. The k-split algorithm can be extended to multi-dimensional distributions, but here we deal with the one-dimensional version only.
The algorithm
The k-split algorithm is an adaptive histogram-type estimate which maintains a good partitioning by doing cell splits. We start out with a histogram range [xlo, xhi) with k equal-sized histogram cells with observation counts n1, n2, ... nk. Each collected observation increments the corresponding observation count. When an observation count ni reaches a split threshold, the cell is split into k smaller, equal-sized cells with observation counts ni,1, ni,2, ... ni,k initialized to zero. The ni observation count is remembered and is called the mother observation count to the newly created cells. Further observations may cause cells to be split further (e.g. ni,1,1,...ni,1,k etc.), thus creating a k-order tree of observation counts where leaves contain live counters that are actually incremented by new observations, and intermediate nodes contain mother observation counts for their children. If an observation falls outside the histogram range, the range is extended in a natural manner by inserting new level(s) at the top of the tree. The fundamental parameter to the algorithm is the split factor k. Low values of k, k=2 and k=3 are to be considered. In this paper we examine only the k=2 case.
For density estimation, the total number of observations that fell into each cell of the partition has to be determined. For this purpose, mother observations in each internal node of the tree must be distributed among its child cells and propagated up to the leaves.
Let n...,i be the (mother) observation count for a cell, s...,i be the total observation count in a cell (n...,i plus the observation counts in all its sub-, sub-sub-, etc. cells), and m...,i the mother observations propagated to the cell. We are interested in the ñ...,i = n...,i + m...,i estimated amount of observations in the tree nodes, especially in the leaves. In other words, if we have ñ...,i estimated observation amount in a cell, how to divide it to obtain m...,i,1, m...,i,2 ... m...,i,k that can be propagated to child cells. Naturally, m...,i,1 + m...,i,2 +...+ m...,i,k = ñ...,i.
Two natural distribution methods are even distribution (when m...,i,1 = m...,i,2 =...= m...,i,k) and proportional distribution (when m...,i,1 : m...,i,2 : ... : m...,i,k = s...,i,1 : s...,i,2 : ... : s...,i,k). Even distribution is optimal when the s...,i,j values are very small, and proportional distribution is good when the s...,i,j values are large compared to m...,i,j. In practice, a linear combination of them seems appropriate, where =0 means even and =1 means proportional distribution:
, [0,1]
Note that while n...,i are integers, m...,i and thus ñ...,i are typically real numbers. The histogram estimate calculated from k-split is not exact, because the frequency counts calculated in the above manner contain a degree of estimation themselves. This introduces a certain cell division error; the parameter should be selected so that it minimizes that error. It has been shown that the cell division error can be reduced to a more-than-acceptable small value.
Strictly speaking, the k-split algorithm is semi-online, because its needs some observations to set up the initial histogram range. However, because of the range extension and cell split capabilities, the algorithm is not very sensitive to the choice of the initial range, so very few observations are enough for range estimation (say Npre=10). Thus we can regard k-split as an on-line method.
K-split can also be used in semi-online mode, when the algorithm is only used to create an optimal partition from a larger number of Npre observations. When the partition has been created, the observation counts are cleared and the Npre observations are fed into k-split once again. This way all mother (non-leaf) observation counts will be zero and the cell division error is eliminated. It has been shown that the partition created by k-split can be better than both the equi-distant and the equal-frequency partition.
OMNeT++ contains an experimental implementation of the k-split algorithm, the cKSplit class. Research on k-split is still under way.
The cKSplit class
TBD comments
Member functions:
void setCritFunc(KSplitCritFunc _critfunc, double *_critdata);
void setDivFunc(KSplitDivFunc _divfunc, double *_divdata);
void rangeExtension( bool enabled );
struct sGrid
{
int parent; // index of parent grid
int reldepth; // depth = (reldepth - rootgrid's reldepth)
long total; // sum of cells & all subgrids (includes 'mother')
int mother; // observations 'inherited' from mother cell
int cells[K]; // cell values
};
int treeDepth();
int treeDepth(sGrid& grid);
double realCellValue(sGrid& grid, int cell);
void printGrids();
sGrid& grid(int k);
sGrid& rootGrid();
In many simulations, only the steady state performance (i.e. the performance after the system has reached a stable state) is of interest. The initial part of the simulation is called the transient period. After the model has entered steady state, simulation must proceed until enough statistical data have been collected to compute result with the required accuracy.
Detection of the end of the transient period and a certain result accuracy is supported by OMNeT++. The user can attach transient detection and result accuracy objects to a result object (cStatistic's descendants). The transient detection and result accuracy objects will do the specific algorithms on the data fed into the result object and tell if the transient period is over or the result accuracy has been reached.
The base classes for classes implementing specific transient detection and result accuracy detection algorithms are:
Basic usage
TBD comments
Attaching detection objects to a cStatistic and getting pointers to the attached objects:
addTransientDetection(cTransientDetection *object); addAccuracyDetection(cAccuracyDetection *object); cTransientDetection *transientDetectionObject(); cAccuracyDetection *accuracyDetectionObject();
Detecting the end of the period:
Transient detection
Currently one transient detection algorithm is implemented, i.e. there's one class derived from cTransientDetection. The cTDExpandingWindows class uses the sliding window approach with two windows, and checks the difference of the two averages to see if the transient period is over.
void setParameters(int reps=3,
int minw=4,
double wind=1.3,
double acc=0.3);
Accuracy detection
Currently one transient detection algorithm is implemented, i.e. there's one class derived from cAccuracyDetection. The algorithm implemented in the cADByStddev class is: divide the standard deviation by the square of the number of values and check if this is small enough.
void setParameters(double acc=0.1,
int reps=3);
Objects of type cOutVector are responsible for writing time series data (referred to as output vectors) to a file. The record() member is used to output a value (or a value pair) with a timestamp.
It can be used like this:
cOutVector resp_v("response time");
while (...)
{
double response_time;
//...
resp_v.record( response_time );
//...
}
All cOutVector objects write to the same, common file. The file is textual; each record() call generates a line in the file. The output file can be processed using Plove, but otherwise its simple format allows it to be easily processed with sed, awk, grep and the like, and it can be imported by spreadsheet programs. The file format is described later in this manual (in the section about simulation execution).
You can disable the output vector or specify a simulation time interval for recording either from the ini file or directly from program code:
cOutVector v("v");
simtime_t t = ...;
v.enable();
v.disable();
v.setStartTime( t );
v.setStopTime( t+100.0 );
If the output vector object is disabled or the simulation time is outside the specified interval, record() doesn't write anything to the output file. However, if you have a Tkenv inspector window open for the output vector object, the values will be displayed there, regardless of the state of the output vector object.
While output vectors are to record time series data and thus they typically record a large volume of data during a simulation run, output scalars are supposed to record a single value per simulation run. You can use outputs scalars
Output scalars are recorded with the recordScalar() member function. It is overloaded, you can use it to write doubles and strings (const char *):
double avg_throughput = total_bits / simTime();
recordScalar("Average throughput", avg_throughput);
You can record whole statistics objects by calling recordStats():
cStdDev *eedstats = new cStdDev;
....
recordStats("End-to-end Statistics", eedstats);
Calls to recordScalar() and recordStats() are usually placed in the redefined finish() member function of a simple module.
The above calls write into the (textual) output scalar file. The output scalar file is preserved across simulation runs (unlike the output vector file is, scalar files are not deleted at the beginning of each run). Data are always appended at the end of the file, and output from different simulation runs are separated by special lines.
Nearly all classes in the simulation class library are descendants of cObject. If you want to derive a new class from cObject or a cObject descendant, you must redefine some member functions so that objects of the new type can fully co-operate with other parts of the simulation system. A more-or-less complete list of these functions are presented here. Do not be embarrassed at the length of the list: most functions are not absolutely necessary to implement. For example, you do not need to redefine forEach() unless your class is a container class.
One should also use the Register_Class() macro to register the new class. It is used by the createOne() function and the PVM extension of OMNeT++.
Let us see a simple example. The header file:
// File: cmyclass.h
#include "cobject.h"
class cMyClass : public cObject
{
public:
int samples;
cMyClass(cMyClass& myclass);
cMyClass(const char *name=NULL, int k=0);
virtual ~cMyClass() {}
virtual const char *className() {return "cMyClass";}
virtual cObject *dup() {return new cMyClass(*this);}
virtual void info(char *buf);
virtual void writeContents(ostream& os);
cMyClass& operator=(cMyClass& myclass);
};
The corresponding .cc file:
// File: cmyclass.cc
#include <stdio.h>
#include <string.h>
#include <iostream.h>
#include "cmyclass.h"
Register_Class( cMyClass );
cMyClass::cMyClass(cMyClass& myclass) : cObject()
{
setName( myclass.name() );
operator=( myclass );
}
cMyClass::cMyClass(const char *name, int k) : cObject( name )
{
samples = k;
}
void cMyClass::info(char *buf)
{
cObject::info( buf );
sprintf( buf+strlen(buf), " samples=0", samples);
}
void cMyClass::writeContents(ostream& os)
{
os << " samples: " << samples << '\n';
}
cMyClass& cMyClass::operator=(cMyClass& myclass)
{
cObject::operator=(myclass);
samples = myclass.samples;
}
See the virtual functions of cObject in the class library reference for more information.
The global object called ev represents the user interface of the simulation program. You can send data to ev using the C++-style I/O operator (<<).
ev << "started\n"; ev << "about to send message #" << i << endl; ev << "queue full, discarding packet\n";
The exact way messages are displayed to the user depends on the user interface. In the command-line user interface (Cmdenv), it is simply dumped to the standard output. (This output can also be disabled from the ini file so that it doesn't slow down simulation when it is not needed.) In windowing user interfaces (Tkenv), each simple module can have a separate text output window.
The above means that you should not use printf, cout << and the like because with Tkenv, their output would appear in the xterm window behind the graphical window of the simulation application.
The user can also specify a phase string that is displayed at the top of the text output windows. The phase string can indicate what the module is currently doing.
setPhase("starting up");
for(;;)
{
setPhase("idle");
//...
setPhase("opening connection");
ev << "connection request from " << src << "\n";
//..
setPhase("connection alive");
//..
setPhase("closing connection");
//...
}
Writing out informative messages at strategic points of the code is an effective way debugging.
You may want some of your int, long, double, char, etc. variables to be inspectable in Tkenv and to be output into the snapshot file. In this case, you can create cWatch objects for them with the WATCH macro:
int i; WATCH(i); char c; WATCH(c);
Tkenv also lets you change the value of the WATCHed variables.
The WATCH() macro expands to a dynamically created cWatch object. The object remembers the address and type of your variable. The macro expands to something like:
new cWatch("i",i);
You can also make a WATCH for pointers of type char* or cObject*, but this may cause a segmentation fault if the pointer does not point to a valid location when Tkenv or snapshot() wants to use it.
You can also set watches for variables that are members of the module class or for structure fields:
WATCH( lapbconfig.timeout );
Placement of WATCHes
Be careful not to execute a WATCH statement more than once, as each call would create a new cWatch object! If you use activity(), the best place for WATCHes is the top of the activity() function. If you use handleMessage(), place the WATCH() statement into initialize(). WATCH() creates a dynamic cWatch object, and we do not want to create a new object each time handleMessage() is called.
The snapshot() function outputs textual information about all or selected objects of the simulation (including the objects created in module functions by the user) into the snapshot file.
bool snapshot(cObject *obj = &simulation, const char *label = NULL);
The function can be called from module functions, like this:
snapshot(); // dump the whole network snapshot(this); // dump this simple module and all its objects snapshot(&putAsideQueue); // dump queue contents snapshot(&simulation.msgQueue); // dump future events
This will append snapshot information to the end of the snapshot file. (The snapshot file name has an extension of .sna, default is omnetpp.sna. Actual file name can be set in the config file.)
The snapshot file output is detailed enough to be used for debugging the simulation: by regularly calling snapshot(), one can trace how the values of variables, objects changed over the simulation. The arguments: label is a string that will appear in the output file; obj is the object whose inside is of interest. By default, the whole simulation (all modules etc) will be written out.
If you run the simulation with Tkenv, you can also create a snapshot from the menu.
An example of a snapshot file:
================================================
|| SNAPSHOT ||
================================================
| Of object: `simulation'
| Label: `three-station token ring'
| Sim. time: 0.0576872457 ( 57ms)
| Network: `token'
| Run no. 1
| Started at: Mar 13, 1997, 14:23:38
| Time: Mar 13, 1997, 14:27:10
| Elapsed: 5 sec
| Initiated by: operator
================================================
(cSimulation) `simulation' begin
Modules in the network:
`token' #1 (TokenRing)
`comp[0]' #2 (Computer)
`mac' #3 (TokenRingMAC)
`gen' #4 (Generator)
`sink' #5 (Sink)
`comp[1]' #6 (Computer)
`mac' #7 (TokenRingMAC)
`gen' #8 (Generator)
`sink' #9 (Sink)
`comp[2]' #10 (Computer)
`mac' #11 (TokenRingMAC)
`gen' #12 (Generator)
`sink' #13 (Sink)
end
(cCompoundModule) `token' begin
#1 params (cArray) (n=6)
#1 gates (cArray) (empty)
comp[0] (cCompoundModule,#2)
comp[1] (cCompoundModule,#6)
comp[2] (cCompoundModule,#10)
end
(cArray) `token.parameters' begin
num_stations (cModulePar) 3 (L)
num_messages (cModulePar) 10000 (L)
ia_time (cModulePar) truncnormal(0.005,0.003) (F)
THT (cModulePar) 0.01 (D)
data_rate (cModulePar) 4000000 (L)
cable_delay (cModulePar) 1e-06 (D)
end
(cModulePar) `token.num_stations' begin
Type: L
Value: 3
end
[...token.num_messages omitted...]
(cModulePar) `token.ia_time' begin
Type: F
Value: truncnormal(0.005,0.003)
end
[...rest of parameters & gates stuff deleted from here...]
(cCompoundModule) `token.comp[0]' begin
parameters (cArray) (empty)
gates (cArray) (n=2)
mac (TokenRingMAC,#3)
gen (Generator,#4)
sink (Sink,#5)
end
(cArray) `token.comp[0].parameters' begin
end
(cArray) `token.comp[0].gates' begin
in (cGate) <-- comp[2].out
out (cGate) --> D --> comp[1].in
end
(cGate) `token.comp[0].in' begin
type: input
inside connection: token.comp[0].mac.phy_in
outside connection: token.comp[2].out
delay: -
error: -
data rate: -
end
(cGate) `token.comp[0].out' begin
type: output
inside connection: token.comp[0].mac.phy_out
outside connection: token.comp[1].in
delay: (cPar) 1e-06 (D)
error: -
data rate: -
end
(TokenRingMAC) `token.comp[0].mac' begin
parameters (cArray) (n=2)
gates (cArray) (n=4)
local-objects (cHead)
class-data-members (cHead)
putaside-queue (cQueue) (empty)
end
[...comp[0].mac parameters stuff deleted from here...]
(cArray) `token.comp[0].mac.gates' begin
phy_in (cGate) <-- <parent>.in
from_gen (cGate) <-- gen.out
phy_out (cGate) --> <parent>.out
to_sink (cGate) --> sink.in
end
[...detailed gate list deleted from here...]
(cHead) `token.comp[0].mac.local-objects' begin
sendqueue-length (cOutVector) (single)
send-queue (cQueue) (n=11)
end
(cOutVector) `token.comp[0].mac.local-objects.sendqueue-length' begin
end
(cQueue) `token.comp[0].mac.local-objects.send-queue' begin
0-->1 (cMessage) Tarr=0.0158105774 ( 15ms) Src=#4 Dest=#3
0-->2 (cMessage) Tarr=0.0163553310 ( 16ms) Src=#4 Dest=#3
0-->1 (cMessage) Tarr=0.0205628236 ( 20ms) Src=#4 Dest=#3
0-->2 (cMessage) Tarr=0.0242203591 ( 24ms) Src=#4 Dest=#3
0-->2 (cMessage) Tarr=0.0300994268 ( 30ms) Src=#4 Dest=#3
0-->1 (cMessage) Tarr=0.0364005251 ( 36ms) Src=#4 Dest=#3
0-->1 (cMessage) Tarr=0.0370745702 ( 37ms) Src=#4 Dest=#3
0-->2 (cMessage) Tarr=0.0387984129 ( 38ms) Src=#4 Dest=#3
0-->1 (cMessage) Tarr=0.0457462493 ( 45ms) Src=#4 Dest=#3
0-->2 (cMessage) Tarr=0.0487308918 ( 48ms) Src=#4 Dest=#3
0-->2 (cMessage) Tarr=0.0514466766 ( 51ms) Src=#4 Dest=#3
end
(cMessage) `token.comp[0].mac.local-objects.send-queue.0-->1' begin
#4 --> #3
sent: 0.0158105774 ( 15ms)
arrived: 0.0158105774 ( 15ms)
length: 33536
kind: 0
priority: 0
error: FALSE
time stamp:0.0000000 ( 0.00s)
parameter list:
dest (cPar) 1 (L)
source (cPar) 0 (L)
gentime (cPar) 0.0158106 (D)
end
(cArray) `token.comp[0].mac.local-objects.send-queue.0-->1.par-vector' begin
dest (cPar) 1 (L)
source (cPar) 0 (L)
gentime (cPar) 0.0158106 (D)
end
[...message parameters and the other messages' stuff deleted...]
(cHead) `token.comp[0].mac.class-data-members' begin
end
(cQueue) `token.comp[0].mac.putaside-queue' begin
end
[...comp[0].gen and comp[0].sink stuff deleted from here...]
[...whole comp[1] and comp[2] stuff deleted from here...]
(cMessageHeap) `simulation.message-queue' begin
1-->0 (cMessage) Tarr=0.0576872457 ( 57ms) Src=#8 Dest=#7
(cMessage) Tarr=0.0577201630 ( 57ms) Mod=#8 (selfmsg)
(cMessage) Tarr=0.0585677054 ( 58ms) Mod=#4 (selfmsg)
(cMessage) Tarr=0.0594939072 ( 59ms) Mod=#12 (selfmsg)
(cMessage) Tarr=0.0601010000 ( 60ms) Mod=#7 (selfmsg)
1-->2 (cMessage) Tarr=0.0601020000 ( 60ms) Src=#11 Dest=#13
end
[...detailed list of message queue contents deleted from here...]
To reduce the size of the file, you may well decide to make a snapshot only of a part of the model. This example reports only about the current simple module's put-aside queue:
snapshot(&putAsideQueue);
With activity() only! In those user interfaces which support debugging, breakpoints stop execution and the state of the simulation can be examined.
You can set a breakpoint inserting a breakpoint() call into the source:
for(;;)
{
cMessage *msg = receive();
breakpoint("before-processing");
//..
breakpoint("before-send");
send( reply_msg, "out" );
//..
}
In user interfaces that do not support debugging, breakpoint() calls are simply ignored.
Some container classes and functions suspend the simulation and issue warning messages in potentially bogus/dangerous situations, for example when an object is not found and NULL pointer/reference is about to be returned. Very often this is useful, but sometimes it is more trouble. You can turn warnings on/off from the ini file (warnings=yes/no).
It is a good practice to leave warnings enabled, and temporarily disable warnings in places where OMNeT++ would normally issue warnings but you know the code is correct. This is done in the following way:
bool w = simulation.warnings(); simulation.setWarnings( false ); ... ... // critical code ... simulation.setWarnings( w );
If is important to choose the correct stack size for modules. If the stack is too large, it unnecessarily consumes memory; if it is too small, stack violation occurs.
From the Feb99 release, OMNeT++ contains a mechanism that detects stack overflows. It checks the intactness of a predefined byte pattern (0xdeadbeef) at the stack boundary, and reports "stack violation" if it was overwritten. The mechanism usually works fine, but occasionally it can be fooled by large -- and not fully used -- local variables (e.g. char buffer[256]): if the byte pattern happens to fall in the middle of such a local variable, it may be preserved intact and OMNeT++ does not detect the stack violation.
To be able to make a good guess about stack size, you can use the stackUsage() call which tells you how much stack the module actually uses. It is most conveniently called from finish():
void FooModule::finish()
{
ev << stackUsage() << "bytes of stack used\n";
}
The value includes the extra stack added by the user interface library (see extraStackforEnvir in envir/omnetapp.h), which is currently 8K for Cmdenv and 16K for Tkenv.
stackUsage()also works by checking the existence of predefined byte patterns in the stack area, so it is also subject to the above effect with local variables.
Sometimes it is useful to change the appearance of some components in the network graphics, such as the color of the modules, color/width of connection arrows etc.
The appearance of nodes and connections is determined by the display strings. Display strings are initially taken from the NED description (stuff like: display: "p=100,10;i=pc" ). You can change the display string of a module or connection arrow at run-time by calling setDisplayString(). The display string of a connection arrow is stored in its source gate. Display string changes will immediately take effect.
Examples:
setDisplayString(dispSUBMOD,"p=100,100;b=60,30,rect;o=red,black,3");
parentModule()->setDisplayString(dispSUBMOD,"p=100.....");
gate("out")->setDisplayString("o=yellow,3");
TBD 'immediate' arg to setDisplayString(); cMessage's setDisplayString().
Here are a few tips that can help you make the simulation faster:
Two techniques are discussed here in detail:
In a complex simulation, a lot of messages are created, sent and destroyed. Messages typically have some parameters attached to them as cPar objects and it frequently happens that a certain parameter has identical values in all messages (for example, source address in a frame is the same in all messages sent by one module). Still, separate parameter objects are created and destroyed with each message, which is very costly. One could save significant amount of CPU time and memory if a single object could serve as a parameter to all existing messages.
This can be achieved with proper ownership control. See the following example:
void MyComputer::activity()
{
cPar source_addr; // address of this node
cPar dest_addrs[3]; // possible destinations
source_addr.setStringValue( "DECnet000728" );
dest_addrs[0].setStringValue( "cisco_F99030" );
dest_addrs[1].setStringValue( "DEC___28E6AD" );
dest_addrs[2].setStringValue( "DECnet000B04" );
long k=0;
for(;;)
{
cMessage *packet = new cMessage("DATA");
packet->addPar( *new cPar("sequence", 'L', k++) );
packet->parList().takeOwnership( false ); // NOTE THIS LINE!!!
packet->addPar( source_addr );
packet->addPar( dest_addrs[ k ] );
send(packet, "output-gate");
wait( truncnormal(1.5, 0.5) );
}
}
The above simple module code models the message generation part of a computer on a LAN. The module sends out messages (packets) to different stations in every 1.5 seconds or so. The messages have three parameters: the source address, the destination address and a sequence number. The source address is the same in each packet, and there are only three possible destination stations. The sequence number is different in each packet.
To avoid the overhead caused by having to create source and destination address objects for each message, the module creates these objects only once; they will be shared among all messages. Separate sequence number objects are created for each message.
Let us see what happens to the sequence number object when it is inserted into the message. The message object, by default, takes the ownership of the object. Ownership means the responsibility of destruction; that is, when the message is destroyed, the parameter object will be destroyed as well.
This is exactly what we need most of the time. But if we just added the shared source and destination address objects to a message, then we would have problems when the message is destroyed. Somehow it must be told to the message object to leave our shared parameters alone and not to become their owner. This is exactly what the
packet->parList().takeOwnership(false);
line does: it sets a flag that tells the message (to be more precise, to its internal parameter list object) not to take the ownership of objects that will be inserted from then on. It does not affect objects already inserted. As a result, all messages will just hold pointers to the shared cPar objects and never do any harm to them.
The above example shows that with CPU-intensive simulations, you can save a lot of computation time and memory just by using the ownership mechanism already present in OMNeT++.
There are situations when using NED files to describe network topology is inconvenient, for example because the topology information comes from an external source (e.g. it is exported from a network management program). In such case, you have two possibilities to avoid writing NED files by hand:
The two solutions have different advantages and disadvantages. The first is more useful in the model development phase, while the second one is better for writing larger scale, more productized simulation programs. In the next sections we examine both methods.
Text processing programs like awk or perl are excellent tools to read in textual data files and generate NED files from them. Perl also has extensions to access SQL databases, so it can also be used if the network topology is stored in a database.
The advantage is that the necessary awk or perl program can be written in a releatively short time, and it is inexpensive to maintain afterwards: if the structure of the data files change, the NED-creating program can be easily modified. The disadvantage is that the resulting NED files are often quite big and the C++ compilation of the *_n.cc files take too long.
This method is best suited in the first phase of a simulation project when the topology, the format of the data files, etc. have not yet settled down.
Another alternative is to write C++ code which becomes part of the simulation executable. The code would read the topology data from data files or a database, and build the network directly. The code which builds the network would be quite similar to the *_n.cc files output by nedc.
Since writing such code is more complex than letting perl generate NED files, this method is recommended when the simulation program has to be somewhat more productized, for example when OMNeT++ and the simulation model is embedded into a larger program, e.g. a network design tool.
As it was already mentioned, an OMNeT++ model physically consists of the following parts:
Model files are usually placed in the projects/modelname subdirectory of the main OMNeT++ directory.
The NED files are compiled into C++ using the NEDC compiler which is part of OMNeT++. The NEDC compiler (source and executable) is normally located in the nedc subdirectory of the main OMNeT++ directory.
The simulation system provides the following components that will be part of the simulation executable:
Simulation programs are built from the above components. First, the NED files are compiled into C++ source code using the NEDC compiler. Then all C++ sources are compiled and linked with the simulation kernel and a user interface to form a simulation executable.
The following figure gives an overview of the process of building
and running simulation programs.
This section discusses how to use the simulation system on the following platforms:
Installation essentially consists of building NEDC and the necessary libraries.
After you've unpacked the distribution in your home directory, just type the following commands:
cd ~/omnetpp ./configure ./make
It will build all the libraries and the example programs for you. If you have a C++ compiler other then gcc, you'll have to edit the configure script.
The makemake script can automatically generate the makefile for your simulation program, based on the source files it finds in your directory. makemake has several options; type
makemake -h
to see them.
To be able to use makemake, you have to collect all your sources (.ned, .cc, .h files) in one directory. (Large models which spread across several directories are covered later in this section.)
Then type
makemake
If you already had a makefile in that directory, you'll have to force makemake to overwrite it:
makemake -f
If you have problems, check the path definitions (locations of include files and libraries etc.) in the configure script and correct them if necessary. Then re-run configure to commit the changes to all makefiles, the makemake script etc.
You can specify the user interface (Cmdenv/Tkenv) with the -u option (with no -u, Tkenv is the default):
makemake -u Tkenv
The name of the output file is set with the -o option (the default is the name of the directory):
makemake -o fddi-net
If some of your source files are generated from other files (for example, you use machine-generated NED files), write your make rules into a file called makefrag. When you run makemake, it will automatically insert makefrag into the resulting makefile. With the -i option, you can also name other files to be included into makefile.
In the case of a large project, your source files may be spread across several directories. You have to decide whether you want to use static linking, shared or run-time loaded libraries. Here we discuss static linking.
In each subdirectory (say trafgen/ and router/), run
makemake -n
The -n option means no linking is necessary, only compiling has to be done.
In your toplevel source directory, run
makemake trafgen/ router/
This results in recursive makefiles: when you build the simulation, make will descend into trafgen/ and router/, run make in both, then it will link an executable with the object files in the two directories.
You may need to use the -I option if you include files from other directories. The -I option is for both C++ and NED files. In our example, you could run
makemake -n -I../router
in the trafgen/ directory and vica versa.
If you're willing to play with shared and run-time loaded libraries, several makemake options and the [General]/load-libs= ini file option leave you enough room to do so.
Default linking uses the shared libraries. One reason you would want static linking is that debugging the OMNeT++ class library is more trouble with shared libraries. Another reason might be that you want to run the executable on another machine without having to worry about setting LD_LIBRARY_PATH.
If you want static linking, find the
build_shared_libs=yes
line in the configure.user script and change it to
build_shared_libs=no
Then you have to re-run the configure script and rebuild everything:
./configure make clean make
IDE files are provided for all OMNeT++ libraries (Sim, Envir, Cmdenv, Tkenv). You should be able to build the .lib files from the Borland C++ IDE without problem. You may need to adjust the include and lib paths in Options|Project --> Directories.
Tkenv needs special treatment. You need to get the Tcl and Tk sources (I tried with 8.0 patchlevel 2: tcl80p2.zip and tk80p2.zip), build the libraries and install them properly. Tkenv includes the Tk header files so before compiling Tkenv, you may need to adjust the paths in Options|Project --> Directories.
The default location of Tkenv's TCL script parts and the bitmaps directory is passed to the Tkenv as a compile-time external define. (At run-time, they can be overridden be the OMNETPP_TKENV_DIR and OMNETPP_BITMAP_PATH environment variables.) You'll probably need to adjust those paths too: they are in Options|Project --> Compiler --> Defines.
To compile nedc, you'll need to get a bison/flex pair that works in the Win95/NT command box. Alternatively, you can compile it in DOS with Borland C++ 3.1 and with bison/flex from the DJGPP package.
It is best starting by copying an example simulation's project file to a different name and modify that. What you will need to have in your project file:
If you started out from a project file in the distribution, you can easily switch from one user interface to another by selecting the Exclude From Parent option for exactly of the omnetpp-cmdenv and omnetpp-tkenv source pools (right click --> Edit Local Options --> Topics/Build Attributes --> Exclude from parent).
If you're going to build a LARGE model, be sure to increase the stack size in Options|Project options|Linker|32-bit Linker|Reserved stack size. The default is 0x1000000 (1MB), which is hardly enough for OMNeT++ simulations. Increase it to 64MB for example: 0x40000000. If the simulation exceeds the stack size configured here, you'll get nice exceptions, General Protection Faults and the like.
If you want to make your own project files (maybe for porting to another compiler?), here are some hints:
Name: NEDCompile
Path: ..\..\src\nedc\nedc.exe
Command Line: $NOSWAP $CAP MSG(BORL2MSG) $EDNAME
Menu Text: NED Compile
Help Hint: OMNeT++ NED compiler
Select Advanced, and fill in the dialog:
Type: Translator
Translate From: .ned
Translate To: .cc
Default For: .ned
Download and install Tcl/Tk. You need at least version 8.0p1, but it's better to download the latest version (currently 8.2).
Download the omnetpp source code and documentation from the omnetpp site. If you downloaded a zip file, extract its contents to the installation directory. If you downloaded a tgz file, copy it to the directory where you want to install OMNeT++, and extract the archive using the command:
tar zxvf omnetpp.tgz
A subdirectory called something like omnetpp-2.0 will be created which will contain the simulator files. In this text we'll assume this directory is c:\omnetpp-2.0.
Make sure the MSVC executables (nmake,cl,link,...) are in the path. It is also recommended that you put the c:\omnetpp-2.0\bin directory into the path.
Check the configuser.vc file to make sure it contains the proper settings. You'll probably need to correct the paths in the following lines:
OMNETPP_ROOT=c:\omnetpp-2.0 TK_INCL_DIR=c:\tcl\include TK_LIB_DIR=c:\tcl\lib
and maybe also the file names in the definition a few lines further below:
WISH=$(TK_LIB_DIR)/../bin/wish82.exe ... TK_LIBS=tcl82.lib tk82.lib /libpath:"$(TK_LIB_DIR)"
Then type
nmake -f Makefile.vc
This should build the executables and the libraries and copy them to the bin and lib subdirectories within the top-level OMNeT++ directory.
Unfortunately MSVC doesn't like the .cc extension, so first you have to rename the .cc files to .cpp. You can do that with samples/cc2cpp.bat.
To link executables with Tkenv, the MSVC project files need the location of the Tcl/Tk dlls & libs. Instead of having the path hardwired, the project files expect to find it in the TK_LIB_PATH environment variable, so you have to set it before starting MSVC.
The names of the Tcl/Tk libs is assumed to be tcl82.lib and tk82.lib -- if you use a different version, you'll have to manually change the project files.
To build a sample simulation, start MSVC and open the project (.dsp) file. The simulation should build without any project file adjustment.
To change from Tkenv to Cmdenv or vica versa, choose Build|Set active configuration from the menu and select one of 'Debug-Tkenv', 'Release-Tkenv', 'Debug-Cmdenv', 'Release-Cmdenv', then re-link the executable.
If you have big models, you'll probably have to increase the stack size. You'll find the setting under Project|Settings --> 'Link' tab --> choose 'Output' from combo --> Stack allocations, Reserve. Be aware that if you don't specify anything here, MSVC defaults to 1MB -- way too small.
If you need to modify the names of the Tcl/Tk libs (because you installed a Tcl/Tk version other than 8.2), see Project|Settings --> 'Link' tab --> choose 'Input' from combo --> Libraries.
The Tcl/Tk install program normally sets the TCL_LIBRARY environment variable needed by Tcl applications. However, if you see the "can't find a usable init.tcl..." error message when you start a simulation program (or Gned or Plove), then that didn't happen and you have to set the variable yourself.
1. Start by copying & renaming one of the .dsp files from the samples directory. It already contains the Tkenv/Cmdenv configurations, etc.
2. Rename all .cc files to .cpp (ren *.cc *.cpp) and add them to the project.
3. Add the .ned files to the project and set custom build option for them:
Description: NED Compiling $(InputPath) Command: nedc -s _n.cpp $(InputPath) Outputs: $(InputPath)_n.cpp
Hint: you can select all .ned files together, and 'All configurations' from the combo at the left of the Settings dialog, and then you have to type this settings only once.
4. For each .ned file, add a corresponding _n.cpp file.
Hint: if you compile the .ned files (choose 'Compile' from the menu), the _n.cpp files will be created, and you can select them all at once in the 'Add files' dialog.
5. Make sure to turn off exception handling and RTTI (they interfere with the coroutine library), and set the necessary reserved stack size.
6. Note: for Tkenv, link with sim_std.lib, envir.lib, tkenv.lib and the Tcl/Tk libraries (link as Win32 Console app...). For Cmdenv, you need to link with sim_std.lib, envir.lib, and cmdenv.lib.
It is planned to create wizards in the future to ease some of these steps.
If you need to recompile the OMNeT++ libraries with different flags (e.g. for debugging or optimized for speed), then cd to the top-level omnetpp directory, edit configuser.vc accordingly, then say:
nmake -f Makefile.vc clean nmake -f Makefile.vc
If you want to recompile just a single library, then cd to the directory of the library and type:
nmake -f Makefile.vc clean nmake -f Makefile.vc
If you want to use Plove, you should download and install Gnuplot. You'll also need a couple of Unix tools like grep and awk, the easiest way to get them is to download and install the Cygwin package from www.cygnus.com. When you have everything installed, start Plove and set the appropriate configuration in Options|External programs. If you entered everything correctly, Plove should work.
A usual caveat is that Gnuplot expects forward slashes in filenames
and Plove supplies backslashes or vica versa (there are multiple
incompatible builds of Gnuplot on NT); if you suspect this might
be the problem, reverse the slash/backslash setting in Options|External
programs.
An OMNeT++ executable accepts the following command line switches:
-h The program prints a short help message and the networks contained in the executable and exits.
-f<fileName> Specify the name of the configuration file. The default is omnetpp.ini. Multiple -f switches can be given; this allows you to partition your configuration file. For example, one file can contain your general settings, another one most of the module parameters, another one the module parameters you change often.
-l<fileName> Load a shared object (.so file on Unix). Multiple -l switches are accepted. Your .so files may contain module code etc. By dynamically loading all simple module code and compiled network description (_n.o files on Unix) you can even eliminate the need to re-link the simulation program after each change in a source file. (Shared objects can be created with gcc -shared ...)
-r<runs> Only recognized by simulations linked with Cmdenv. It specifies which runs should be executed (e.g. -r2,4,6-8). This option overrides the runs-to-execute= option in the [Cmdenv] section of the ini file (see later).
All other options are read from the configuration file.
An example of running an OMNeT++ executable with the -h flag:
C:\OMNETPP\PROJECTS\FDDI>fddi.exe -h OMNeT++ Discrete Simulation, TUB Dept. of Telecommunications, 1990-97 Networks in this program: 1. NRing 2. FDDI1 End run of OMNeT++
The configuration file (also called ini file, because it has an .ini extension) contains options that control how the simulation is executed and can also contain settings of model parameters. The ini file is a text file consisting of entries grouped into different sections. The following sections can exist:
[General] [Cmdenv], [Tkenv],... [Parameters] [OutVectors] [DisplayStrings] [Machines] [Slaves] [Run 1], [Run 2], [Run 3],...
'#' and ';' denote comments. A sample ini file:
# omnetpp.ini [General] ini-warnings = false network = token distributed = no snapshot-file = token.sna output-vector-file = token.vec log-parchanges = no parchange-file = token.pch random-seed = 1 sim-time-limit = 1000ms cpu-time-limit = 180s total-stack-kb = 2048 [Cmdenv] runs-to-execute = 1-3,5 module-messages = yes verbose-simulation = no display-update = 100ms [Parameters] token.num_stations = 3 token.num_messages = 10000 [Run 1] token.wait_time = 10ms [Run 2] token.wait_time = 30ms
Parameters that were set to input value in the NED file are searched for in the ini file.
OMNeT++ can execute several simulation runs automatically one after another. If multiple runs are selected, option settings and parameter values can be given either individually for each run, or together for all runs, depending in which section the option or parameter appears.
This is summarized in the following table:
| What | If set for all runs together | If set for individual runs |
| general settings | [General] | [Run 1], [Run 2] etc. |
| user interface-specific settings | [Cmdenv], [Tkenv] etc. | [Run 1], [Run 2] etc. |
| module parameter values | [Parameters] | [Run 1], [Run 2] etc. |
| output vector configuration | [OutVectors] | [Run 1], [Run 2] etc. |
| graphical appearance | [DisplayStrings] | [Run 1], [Run 2] etc. |
| logical - physical machine mappings | [Machines] | not possible |
| with distributed execution: settings for slave processes | [Slaves] | [Run 1], [Run 2] etc. |
The most important options of the [General] section are the following.
Almost any of the above options can also be specified individually for each run. Per-run settings (if they exist) have priority over globally set one.
OMNeT++ supports file inclusion in ini files. This feature allows you to partition large ini files to logical units, fixed and varying part etc.
An example:
# omnetpp.ini ... include parameters.ini include per-run-pars.ini ...
Values for module parameters go into the [Parameters] or the [Run 1], [Run 2] etc. sections of the ini file. The run-specific settings take precedence over the overall settings. Parameters that were assigned a (non-input) value in the NED file are not influenced by ini file settings.
Wildcards (*,?) can be used to supply values to several model parameters at a time. Filename-style (glob) and not regex-style pattern matching is used. Character ranges use curly braces instead of square brackets to avoid interference with the notation of module vectors: {a-zA-Z}. If a parameter name matches several wildcards-patterns, the first matching occurrence is used.
An example ini file:
# omnetpp.ini [Parameters] token.num_stations = 3 token.num_messages = 10000 [Run 1] token.stations[*].wait_time = 10ms [Run 2] token.stations[0].wait_time = 5ms token.stations[*].wait_time = 1000ms
As a simulation program is evolving, it is becoming capable of collecting more and more statistics. The size of output vector files can easily reach a magnitude of several ten or hundred megabytes, but very often, only some of the recorded statistics are interesting to the analyst.
In OMNeT++, you can control how cOutVector objects record data to disk. You can turn output vectors on/off or you can assign a result collection interval. Output vector configuration is given in the [OutVectors] section of the ini file, or in the [Run 1], [Run 2] etc sections individually for each run. By default, all output vectors are turned on.
Entries configuring output vectors can be like that:
module-pathname.objectname.enabled = yes/no module-pathname.objectname.interval = start..stop module-pathname.objectname.interval = ..stop module-pathname.objectname.interval = start..
The object name is the string passed to cOutVector in its constructor or with the setName() member function.
cOutVector eed("End-to-End Delay",1);
Start and stop values can be any time specification accepted in NED and config files (e.g. 10h 30m 45.2s).
As with parameter names, wildcards are allowed in the object names and module path names.
An example:
# # omnetpp.ini # [OutVectors] *.interval = 1s..60s *.End-to-End Delay.enabled = yes *.Router2.*.enabled = yes *.enabled = no
The above configuration limits collection of all output vectors to the 1s..60s interval, and disables collection of output vectors except all end-to-end delays and the ones in any module called Router2.
It is possible to log all the changes to module parameters into a text file. This can be useful when the simulation contains run-time tuning of one or more module parameters and one wants to have the trajectory documented.
Module parameter logging must be explicitly enabled from the header file if one wants to use it:
[General] log-parchanges = yes parchange-file = token.pch
The format of the parameter change file is similar to the that of the output vector file.
If a parameter is taken by reference by several modules, any change to the parameter will appear in the file under the name of the top-level parameter, no matter which module actually changed it and under what name.
Display strings control the modules' graphical appearance in the Tkenv user interface. Display strings can be assigned to modules, submodules and gates (a connection's display string is stored in its "from" gate). Display strings can be hardcoded into the NED file or specified in the configuration file. (Hardcoded display strings take precedence over the ones given in ini files.) Format of display string are documented in the User Interfaces chapter.
Display strings can appear in the [DisplayStrings] section of the ini file. They are expected as entries in one of the following forms:
moduletype = "..." moduletype.submodulename = "..." moduletype.inputgatename = "..." moduletype.submodulename.outputgatename = "..."
As with parameter names, wildcards are allowed in module types, submodule and gate names.
As is was pointed out earlier, it is of great importance that different simulation runs and different random number sources within one simulation run use non-overlapping sequences of random numbers.
In OMNeT++, you have three choices:
If you decide for automatic seed selection, do not specify any seed value in the ini file. For the random number generators, OMNeT++ will automatically select seeds that are 1,000,000 values apart in the sequence. If you have several runs, each run is started with a fresh set of seeds that are 1,000,000 values apart from the seeds used for previous runs. Since the generation of new seed values is costly, OMNeT++ has a table of precalculated seeds (256 values); if they are all used up, OMNeT++ starts from the beginning of the table again.
Automatic seed selection may not be appropriate for you for several reasons. First, you may need more than 256 seeds values; or, if you use variance reduction techniques, you may want to use the same seeds for several simulation runs. In this case, there is a standalone program to generate appropriate seed values (seedtool will be discussed in the next section), and you can specify the seeds explicitly in the ini file.
The following ini file explicitly initializes two of the random number generators, and uses different seed values for each run:
[Run 1] gen0-seed = 1768507984 gen1-seed = 33648008 [Run 2] gen0-seed = 1082809519 gen1-seed = 703931312 ...
If you want the same seed values for all runs, you will write something like this:
[General] gen0-seed = 1768507984 gen1-seed = 33648008
All other random number generators (2,3,...) will have their seeds automatically assigned.
As a third way, you can also set the seed values from the code of a simple module using genk_randseed(), but I see no reason why you would want to do so.
The exact meaning of the different entries are:
| [General] | |
| ini-warnings = yes | Helps debugging of the ini file. If turned on, OMNeT++ prints out the name of the entries it that it wanted to read but they were not in the ini file. |
| network = | The name of the network to be simulated. |
| distributed = no | Parallel execution or not. |
| number-of-runs = 1 | OMNeT++ will run multiple runs only of you set this value greater then 1; the existence of sections[Run 1], [Run 2] etc. alone is not enough. |
| snapshot-file = omnetpp.sna | Name of the snapshot file. The result of each snapshot() call will be appended to this file. |
| output-vector-file = omnetpp.vec | Name of output vector file. |
| output-scalar-file = omnetpp.sca | Name of output scalar file. |
| pause-in-sendmsg = no | Only makes sense with step-by-step execution. If enabled, OMNeT++ will split send() calls to two steps. |
| warnings = yes | Globally turns on/off simulation runtime warnings. It is advisable to leave this turned on. |
| log-parchanges = no | Specifies whether changes of module parameters should be logged to file. |
| parchange-file = omnetpp.pch | File to save parameter changes to. |
| sim-time-limit = 1000ms | Duration of the simulation in simulation time. |
| cpu-time-limit= 180s | Duration of the simulation in real time. |
| random-seed = 542 | Random number seed for generator 0. Should not be zero. |
| total-stack-kb = 8192 | Specifies the total stack size (sum of all coroutine stacks) in kilobytes. You need to increase this value if you get the "Cannot allocate coroutine stack ..." error. |
| load-libs = | Name of shared libraries (.so files) to load after startup. You can use it to load simple module code etc.
Example: load-libs=../x25/x25.so ../lapb/lapb.so |
| netif-check-freq= | used with parallel execution |
| gen0-seed = 3567 gen1-seed = 4535 ... | Seeds for the given random number generator. They should not be zero. |
| [Cmdenv] | |
| runs-to-execute=1,3-4,6 | Specifies which simulation runs should be executed |
| module-messages = yes/no | Globally enables/disables ev-style messages in simple modules (e.g. ev << "sending\n";). |
| Verbose-simulation = yes/no | Enable/disable banners for each event ("Event #1234, T=..." stuff.) |
| display-update = 100ms | If there would be no display from the simulation execution (both the above options are disabled), OMNeT++ can print out regular messages of the progress. The interval is understood in simulation time. |
| [Tkenv] | |
| default-run = 1 | Specifies which run Tkenv should set up automatically after startup. If there's no default-run= entry or the value is 0, Tkenv will ask which run to set up. |
| use-mainwindow = yes | |
| print-banners = yes | |
| breakpoints-enabled = yes | Specifies whether the simulation should be stopped at each breakpoint() call in the simple modules. |
| update-freq-fast = 10 | |
| update-freq-express = 500 | |
| animation-delay = 0.3s | Delay between steps when you slow-execute the simulation. |
| Animation-enabled = yes | |
| animation-msgnames = yes | |
| animation-msgcolors = yes | |
| animation-speed = 1.0 | |
| [Slaves] | |
| write-slavelog = yes | |
| slavelog-file = slave.log | |
| module-messages = yes | |
| errmsgs-to-console = yes | |
| infomsgs-to-console = no | |
| modmsgs-to-console = no | |
For selecting good seeds, the seedtool program can be used (it is in the utils directory). When started without command-line arguments, the program prints out the following help:
seedtool - part of the OMNeT++ Simulation System, BME-HIT 1997
A tool to help select good random number generator seed values.
Usage:
seedtool i seed - index of 'seed' in cycle
seedtool s index - seed at index 'index' in cycle
seedtool d seed1 seed2 - distance of 'seed1' and 'seed2' in cycle
seedtool g seed0 dist - generate seed 'dist' away from 'seed0'
seedtool g seed0 dist n - generate 'n' seeds 'dist' apart, starting at
'seed0'
seedtool t - generate hashtable
seedtool p - print out hashtable
The last two options, p and t were used internally to generate a hash table of pre-computed seeds that greatly speeds up the tool. For practical use, the g option is the most important. Suppose you have 4 simulation runs that need two independent random number generators each and you want to start their seeds at least 10,000,000 values apart. The first seed value can be simply 1. You would type the following command:
C:\OMNETPP\UTILS> seedtool g 1 10000000 8
The program outputs 8 numbers that can be used as random number seeds:
1768507984 33648008 1082809519 703931312 1856610745 784675296 426676692 1100642647
You would specify these seed values in the ini file.
TBD Intro, and multiple simulation runs in omnetpp.ini vs controlling script.
Variations over parameter values
You don't need to generate the whole omnetpp.ini from program if you use include files. You can have a fixed omnetpp.ini which contains the line
include parameters.ini
and then generate parameters.ini by program for each run.
Here's the "runall" script of Joel Sherrill's File System Simulator as an example:
#! /bin/bash
#
# This script runs multiple variations of the file system simulator.
#
all_cache_managers="NoCache FIFOCache LRUCache PriorityLRUCache ..."
all_schedulers="FIFOScheduler SSTFScheduler CScanScheduler ..."
for c in ${all_cache_managers}; do
for s in ${all_schedulers}; do
(
echo "[Parameters]"
echo "filesystem.generator_type = \"GenerateFromFile\""
echo "filesystem.iolibrary_type = \"PassThroughIOLibrary\""
echo "filesystem.syscalliface_type = \"PassThroughSysCallIface\""
echo "filesystem.filesystem_type = \"PassThroughFileSystem\""
echo "filesystem.cache_type = \"${c}\""
echo "filesystem.blocktranslator_type = \"NoTranslation\""
echo "filesystem.diskscheduler_type = \"${s}\""
echo "filesystem.accessmanager_type = \"MutexAccessManager\""
echo "filesystem.physicaldisk_type = \"HP97560Disk\""
) >algorithms.ini
./filesystem
done
done
And omnetpp.ini includes algorithms.ini.
Variations over seed value (multiple independent runs)
The same technique can be used if you want several runs with different random seeds. This code should do 500 runs with independent seeds (suppose one run doesn't use more than 10 million random values):
#! /bin/bash
for seed in `seedtool g 1 10000000 500`
do
(
echo "[General]"
echo "random-seed = ${seed}"
echo "output-vector-file = xcube-${seed}.vec"
) >parameters.ini
./xcube
done
omnetpp.ini should include parameters.ini.
Other languages for writing the control script
The above examples use the Unix shell, but you have quite a number of options in what language to implement the controlling script. Some ideas:
The user interface is separated from the simulation kernel; the two parts interact through a well-defined interface. This construction makes it possible to implement several types of user interfaces, without changing the simulation kernel. Also, the same simulation model can be executed with different user interfaces, without any change in the model files themselves. The user would test and debug the simulation with a powerful graphical user interface, and finally run it with a simple and fast user interface that supports batch execution.
User interfaces takes the form of libraries (.a file or .so on UNIX, .lib or .dll file on NT). The libraries are interchangeable. When the user creates a simulation executable, he can pick one of the user interface libraries that he links in.
Three user interfaces have been implemented:
The following sections contain more detailed descriptions about each user interface.
The command line user interface is a small, portable and fast user interface that compiles and runs on all platforms whether it is UNIX, DOS, or WinNT console. Cmdenv is designed primarily for batch execution.
Cmdenv uses simply executes all simulation runs that are described in the configuration file. If one run stops with an error message, subsequent ones will still be executed.
Cmdenv recognizes the following ini file options:
[Cmdenv] runs-to-execute = 1,4-6,8 module-messages = no verbose-simulation = no display-update = 100ms
The first one specifies which runs (described in the [Run 1], [Run 2] etc. sections) should be executed. If the value is missing, Cmdenv executes all runs that have ini file sections; if no runs are specified in the ini file, Cmdenv does one run. The -r command line option overrides this ini file setting.
The second and the third are yes/no settings and control the amount of screen output during simulation. The fourth one is in effect when the other two are disabled (that is, there would be no display at all from the simulation execution); it prints out progress messages at the specified frequency.
Portability: all platforms.
Features
Tkenv is a portable graphical windowing user interface. Tkenv supports interactive execution of the simulation, tracing and debugging. Tkenv is recommended in the development stage of a simulation or for presentation and educational purposes, since it allows one to get a detailed picture of the state of simulation at any point of execution and to follow what happens inside the network. The most important feaures are:
Tkenv makes it possible to view simulation results (output vectors etc.) during execution. Results can be displayed as histograms and time-series diagrams. This can speed up the process of verifying the correct operation of the simulation program and provides a good environment for experimenting with the model during execution. When used together with gdb or xxgdb, Tkenv can speed up debugging a lot.
Portability: Tkenv is built with Tcl/Tk. Tkenv should work on all platforms that Tcl/Tk has been ported to: Unix/X, Win32, Macintosh.
You can get more information about Tcl/Tk in the Web pages listed in the Reference.
Simulation running modes in Tkenv
Tkenv has the following modes for running the simulation :
The running modes have their corresponding buttons on Tkenv's toolbar.
In Step mode, you can execute the simulation event-by-event.
In Run mode, the simulation runs with all tracing aids on. Message animation is active and inspector windows are updated after each event. Output messages are displayed in the main window and module output windows. You can stop the simulation with the Stop button on the toolbar. You can fully interact with the user interface while the simulation is running: you can open inspectors etc.
In Fast mode, animation is turned off. The inspectors and the message output windows are updated after each 10 events (the actual number can be set in Options|Simulation options and also in the ini file). Fast mode is several times faster than the Run mode; the speedup can get close to 10 (or the configured event count).
In Express mode, the simulation runs at about the same speed as with Cmdenv, all tracing disabled. Module output is not recorded in the output windows any more. You can interact with the simulation only once in a while (1000 events is the default as I recall), thus the run-time overhead of the user interface is minimal. You have to explicitly push the Update inspectors button if you want an update.
Inspectors
In Tkenv, objects can be viewed through inspectors. To start, choose Inspect|Network from the menu. Usage should be obvious; just use double-clicks and popup menus that are brought up by right-clicking. In Step, Run and Fast Run modes, inspectors are updated automatically as the simulation progresses. To make ordinary variables (int, double, char etc.) appear in Tkenv, use the WATCH() macro in the C++ code.
In list dialogs, entries begin with text like "ptr0x8000ab7e". Yes, it is really the object pointer; knowing it is extremely useful if you're running the simulation under a debugger such as gdb.
Configuring Tkenv
In case of nonstandard installation, it may be necessary to set the OMNETPP_TKENV_DIR environment variable so that Tkenv can find its parts written in Tcl script.
The default path from where the icons are loaded can be changed with the OMNETPP_BITMAP_PATH variable, which is a semicolon-separated list of directories and defaults to "omnetpp-dir/bitmaps;.;./bitmaps".
The ini file options accepted by Tkenv are:
[Tkenv] use-mainwindow = yes print-banners = yes breakpoints-enabled = yes update-freq-fast = 10 update-freq-express = 500 animation-delay = 0.3s
The above options can also be set from within Tkenv itself, from a configuration dialog box.
Embedding TCL code into the executable
A significant part of Tkenv is written in TCL, in several .tcl script files. The default location of the scripts is passed compile-time to tkapp.cc, and it can be overridden at run-time by the OMNETPP_TKENV_DIR environment variable. The existence of a separate script library can be inconvenient if you want to carry standalone simulation executables to different machines. To solve the problem, there is a possibility to compile the script parts into Tkenv as a large string constant.
The details: the tcl2c program (its C source is there in the Tkenv directory) is used to translate the .tcl files into C code (tclcode.cc), which gets included into tkapp.cc. On Unix, this feature is enabled in Tkenv's makefile; it is documented there exactly how. On Win95/NT, one has to manually compile tcl2c.c into tcl2c.exe, run it to produce tclcode.cc and then compile tkapp.cc without providing the OMNETPP_TKENV_DIR external define. The latter will cause tkapp.cc to include and use tclcode.cc.
There used to be other user interfaces which have been removed from the distribution.
"Stack violation (FooModule stack too small?) in module bar.foo"
OMNeT++ detected that the module has used more stack space than
it has allocated. You should increase the stack for FooModule.
You can call the stackUsage() from finish()
to find out actually how much stack the module used.
"Error: Cannot allocate nn bytes stack for module foo.bar"
If you get the above message, you have to increase the total stack size (the sum of all coroutine stacks). You can do so in omnetpp.ini:
[General] total-stack-kb = 2048 # 2MB
There is no penalty if you set total-stack-kb
too high. I recommend to set it to a few K less than the maximum
process stack size allowed by the operating system (ulimit
-s; see next section).
"Segmentation fault"
On Unix, if you set the total stack size higher, you may get a segmentation fault during network setup (or during execution if you use dynamically created modules) for exceeding the operating system limit for maximum stack size. For example, in Linux 2.0.x, the stack can be at most 8192K (that is, 8MB). The ulimit syscall and utility program can be used to modify the resource limits, but you can only increase if you're root. Furthermore, resource limits are inherited by child processes. The following statement worked out for me under Linux to get a shell with a 64M stack limit:
$ su root Password: # ulimit -s 65536 # su andras $ ulimit -s 65536
If you do not want to go through the above process at each login, you can change the limit in the PAM configuration files. In Redhat Linux (maybe other systems too), add the following line to /etc/pam.d/login:
session required /lib/security/pam_limits.so
and the following line to /etc/security/limits.conf:
* hard stack 65536
A more drastic solution is to recompile the kernel with a larger stack limit. Edit /usr/src/linux/include/linux/sched.h and increase _STK_LIM from (8*1024*1024) to (64*1024*1024).
Finally, it you're tight with memory, you can switch to Cmdenv. Tkenv increases the stack size of each module by about 32K so that user interface code that is called from a simple module's context can be safely executed. Cmdenv does not need that much extra stack.
For investigating memory allocation problems, try using Cmdenv, and uncomment the #defines in src/envir/cmdenv/heap.cc:
HEAPCHECK checks heap on new/delete
COUNTBLOCKS counts blocks on heap and tells it if none left
ALLOCTABLE remembers pointers and reports heap contents if only LASTN blocks remained
DISPLAYALL reports every new/delete
DISPSTRAYS reports deleting of pointers that were not registered by operator new or that were deleted since then
BKPT calls a function at a specified new/delete; you can set a breakpoint to that function
If COUNTBLOCKS is turned on, you should see the [heap.cc-DEBUG:ALL BLOCKS FREED OK] message at the end of the simulation. If you do not see it, it means that some blocks have not been freed up properly, that is, your simulation program is likely to have memory leaks.
If your simulation program is tested and runs OK, you'll probably want to run it as fast as possible. Here's a table that could help where to begin optimizing.
The measurements were made on one version of the FDDI model (you
can find it in the samples directory); we simulated 10
milliseconds. We used Cmdenv. The machine was a 100Mhz Intel Pentium
with 32MB RAM. The simulation program was compiled with Borland
C++ 3.1 (no particular optimization) and run on DOS 6.22. Disk
caching was installed (SmartDrive read/write caching, 8MB cache).
| Settings | Execution time | Details |
| all screen output on; full heapcheck | 7 min 50 sec | Setting in omnetpp.ini:
verbose-simulation = yes The #defines in envir/cmdenv/heap.cc were all enabled. This means full heapcheck with each allocation, tracking of all allocated blocks etc. |
| no screen output at all; full heapcheck | 5 min 50 sec | All screen output were #ifdef'ed out from source; also, the omnetpp.ini contained the
verbose-simulation = no line. The heapcheck defines were turned on. |
| all screen output on; no heapcheck |
2 min | We turned off heapcheck (we commented out the defines in heap.cc) and turned back on the screen output. We used the same omnetpp.ini: setting as with first case. |
| screen output redirected to file; no heapcheck | 15.5 sec | Same as previous configuration, except that we run the program with fddi > output.txt |
| screen output redirected to NUL; no heapcheck | 13 sec | Same as previous configuration, except that we run the program with fddi > NUL |
| screen output turned off from ini file; no heapcheck | 7.5 sec | We did not only redirect but also disabled screen output. Setting in omnetpp.ini:
verbose-simulation = no |
| no screen output generation; no heapcheck | 4.5 sec | We #ifdef'ed out all printouts from the simple module sources and also turned off any messages from omnetpp.ini. |
The moral is that heap checks and screen output greatly influences speed, so once you do not need them (debugging is over), throw them out. You also gain a lot by putting #ifdef lines around your debugging code. And of course, program with care.
Typically, you'll get output vector files as a result of a simulation. Data written to cOutVector objects from simple modules go to output vector files. Normally, you use Plove to look into output vector files and plot vectors in it.
Plove is a handy tool for plotting OMNeT++ output vectors. It uses Gnuplot to do the actual work. You can specify the drawing style (lines, dots etc) for each vector as well as set the most frequent drawing options like axis bounds, scaling, titles and labels etc. You can save the gnuplot graphs to files (postscript, latex, pbm etc) with a click. Plove can also generate standalone shell scripts that plot output vectors in much the same way Plove does itself. These scripts can be used for batch processing or to debug filters (see later). Plove does not take away any of gnuplot's flexibility -- you can embed your own gnuplot commands to customize the output.
Filtering the results before plotting is possible. Filters can do averaging, truncation of extreme values, smoothing, they can do density estimation by calculating histograms etc. Some filters are built in, and you can easily create new filters or modify the existing ones. Filters can be incorporated in one of three ways: as awk expressions, as awk programs and as external filter programs. Filters can be parameterized. Multiple filters for the same vector is not currently supported; also, you cannot currently feed several vectors into a single filter.
Plove does not create temporary files, so you don't need to worry about disk space: if the output vector is there, Plove can plot it for you. Moreover, it can also work with gzipped vector files without extracting them -- just make sure you have zcat.
Plove never modifies the output vector files themselves.
On startup, Plove automatically reads the .ploverc file in your home directory. The file contains general gnuplot settings, the filter configuration etc. (that is, the stuff from the Options menu).
Portability: Plove works fine on Unix and (with some limitations) on Win95/NT.
First, you load an output vector file (.vec) into the left pane. You can also load gzipped vector files (.vec.gz) without having to decompress them. You can copy vectors from the left pane to the right pane by clicking the right arrow icon in the middle. The large PLOT button will plot the selected vectors in the right pane. Selection works as in Windows: dragging and shift+left click selects a range, and ctrl+left click selects/deselects individual items. To adjust drawing style, change vector title or add filter, push the Options... button. This works for several selected vectors too. Plove accepts nc/mc-like keystrokes: F3, F4, F5, F6, F8, grey '+' and grey '*'.
The left pane works as a general storage for vectors you're working with. You can load several vector files, delete vectors you don't want to deal with, rename them etc. All this will not affect the vector files on disk. In the right pane, you can duplicate vectors if you want to filter the vector and also keep the original. If you set the right options for a vector but temporarily do not want it to hang around in the right pane, you can put it back into the left pane for storage.
Filters get an output vector on their standard input (as plain text, with the timestamp being the second and the value being the third field on each line), do some processing to it and write the result to the standard output.
Filters can be incorporated in one of three ways: as awk expressions, as awk programs or as external programs. An 'awk expression' filter means assembling and launching a command like this:
cat foobar.vec | awk '{$3 = <expression>; print}' | ...
An awk program filter means running the following command:
cat foobar.vec | awk '<program>' | ...
The third type of filters is used like this:
cat foobar.vec | <program> <parameters> | ...
Before the filter pipeline is launched, the following substitutions are performed on the awk scripts:
t --> $2 x --> $3
The parameters of the form $(paramname) are also replaced with their actual value.
Thus, if you want to add 1 to all value, you can use the awk expression filter x+1. It will turn into:
awk '{$3 = $3+1}; print'.
When you want to shift the vector by a used-defined DT time, you can create the following awk program filter:
{t += $(DT); print}
Do not forget the print statement, or your filter will not output anything and the gnuplot graph will be empty.
Filters are automatically saved into and loaded from the ~/.ploverc file.
TBD add example scripts
An output vector file contains several series of data produced during simulation. The file is textual, it looks like this:
mysim.vec: vector 1 "subnet[4].term[12]" "response time" 1 1 12.895 2355.66666666 1 14.126 4577.66664666 vector 2 "subnet[4].srvr" "queuelen+queuingtime" 2 2 16.960 2.00000000000 .63663666 1 23.086 2355.66666666 2 24.026 8.00000000000 .44766536
There are label lines (beginning with vector) and data lines.
A vector line introduces a new vector. Its columns are: vector ID, module of creation, name of cOutVector object, multiplicity of data (single numbers or pairs will be written).
Lines beginning with numbers are data lines. The columns: vector ID, current simulation time, and one or two double values.
You can use the Unix grep tool to extract a particular vector from the file. As the first step, you must find out the ID of the vector. You can find the appropriate vector line with a text editor or you can use grep for this purpose:
0rep "queuelen+queuingtime" vector.vec
Or, you can get the list of all vectors in the file by typing:
0rep ^vector vector.vec
This will output the appropriate vector line:
vector 6 "subnet[4].srvr" "queuelen+queuingtime" 2
Pick the vector ID, which is 6 in this case, and grep the file for the vector's data lines:
grep ^6 vector.vec > vector6.vec
Now, vector6.vec contains the appropriate vector. The only potential problem is that the vector ID is there at the beginning of each line and this may be hard to explain to some programs that you use for post-processing and/or visualization. This problem is eliminated by the OMNeT++ splitvec utility (written in awk), to be discussed in the next section.
The splitvec script (part of OMNeT++) breaks the vector file into several files which contain one vector each:
splitvec mysim.vec
creates several files: mysim1.vec, mysim2.vec etc.
mysim1.vec: # vector 1 "subnet[4].term[12]" "response time" 1 12.895 2355.66666666 14.126 4577.66664666 23.086 2355.66666666 mysim2.vec: # vector 2 "subnet[4].srvr" "queuelen+queuingtime" 2 16.960 2.00000000000 .63663666 24.026 8.00000000000 .44766536
As you can see, the vector ID is gone.
The files can be further processed with math packages, or read by analysis or spreadsheet programs which provide numerous ways to display data as diagrams, do calculations on them etc. One could use for example Gnuplot, Matlab, Excel, etc.
Two programs are in common use: Gnuplot and Xmgr. Both are free and both have their good and bad sides; will briefly discuss them. There are innumerable tutorials and documentation about them on the Web; some of them you will find among the References.
Both programs can eat files produced by splitvec. Both programs can produce output in various forms: on screen, in Postscript format, printer files, Latex output etc. For DTP purposes, Postscript seems to be the most appropriate. On Windows, the easiest way is to copy the picture to the clipboard from the Gnuplot window's system menu.
Gnuplot has an interactive command interface. To get the vectors in mysim1.vec and mysim4.vec plotted in the same graph, you can type:
plot "mysim1.vec" with lines, "mysim4.vec" with lines
To adjust the y range, you would type:
set yrange [0:1.2] replot
There are several commands to adjust ranges, plotting style, labels, scaling etc. Gnuplot can also plot 3D graphs. Gnuplot is also available for DOS, Windows and other platforms. Gnuplot also has a simple graphical interactive user interface called PlotMTV. However, we recommend that you use OMNeT++'s Plove tool, described in an earlier section.
Xmgr is an X/Motif based program, with a menu-driven graphical interface. You load the appropriate file by selecting in a dialog box. The icon bar and menu commands can be used to customise the graph. Some say that Xmgr can produce nicer output that Gnuplot and it is easier to use. Xmgr cannot do 3D and only runs on Unixes with X and Motif installed. Xmgr also has a batch interface so you can use it from scripts too.
OMNeT++ supports parallel execution of large simulations. To make use of parallel execution, the model is to be partitioned to several segments that will be simulated independently on different hosts or processors.
The simulation kernel makes it possible to send messages from one segment to another. A message can contain arbitrarily complex data structures; these are transferred transparently, even between hosts of different architectures. The simulation kernel provides a simple synchronization mechanism (syncpoints, available through the syncpoint() call) that can ensure that causality is kept when sending messages between segments. Syncpoints correspond to null messages found in the literature.
Message sending and syncpoints enable one to implement conservative PDES and also a novel and less-known paradigm, Statistical Synchronization. The simulation class library contains objects that explicitly support the implementation of models using Statistical Synchronization.
High level debugging is supported by saving the textual output from remote segments to a log file and/or relaying them to a single console.
OMNeT++ supports flexible partitioning of the model. In the NED language, by using machine parameters you can specify logical hosts for different modules at any level of the module hierarchy of the network. You map logical hosts to physical ones in the ini file; if you map several logical hosts into the same physical machine, they will be merged into a single OMNeT++ process.
The PVM3 (Parallel Virtual Machine Version 3) library is used for communication between hosts. PVM is portable and it is widely used in university and research environment. You can find PVM readings in the Reference.
Overview
When running a simulation in parallel, different segments of the model execute as independent UNIX processes, typically on separate hosts. Since the hosts can be of different speed and the simulated model segments can be of different complexity, at a given moment the model times of different segments will differ: some segments are ahead of the others and some lag behind. Suppose that a message is sent from segment A to segment B which is ahead of A in model time. If B processed the message, causality would break. This should never happen.
The solution built in OMNeT++ is the following. Segment A must know in advance when it will send the next message to segment B and announce it with the syncpoint() call. The simulation kernel sends the syncpoint to segment B. When segment B's model time reaches the specified time, segment B's simulation kernel blocks execution until the promised message arrives from A. Then the simulation continues, typically but not necessarily with the message that has just been received from A.
In the reverse case when A is ahead of B, A's message arrived at B before it has reached the syncpoint. In this case, there is no problem and the syncpoint is just an unnecessary precaution. B just inserts the message in its future event set, clears the syncpoint and continues execution.
The syncpoint API
The syncpoint() call takes two arguments. The first is the model time when (or more precisely: when of after when) the simple module will send a message to another simple module in a different segment. The second argument is a gate given with its number or its name. The gate implicitly specifies the destination segment to synchronize with.
syncpoint(t, "outgate");
Details of the syncpoint implementation
If the destination module is in the same segment, the call is ignored. (This makes it possible to run models designed to execute in parallel as a single process, without any modification.) Each segment keeps a list of syncpoints sent to it (time + gate), ordered by time. Simulation executes normally until it comes to an event that has a time definitely past the first syncpoint in the list. That event is not processed, but the segment goes into a blocked state. While the segment is blocked, it listens for messages arriving from other segments. (In the actual implementation, passive wait is used so a blocked segment doesn't use much CPU time.) Each message that arrives deletes the first syncpoint in the list that matches its gate. The segment goes out of the blocked state when -- because of deletions -- the first syncpoint in the list is no longer past the event in question. Then the simulation goes on normally, either with the newly arrived message (or the earliest of them) or the original event. A message that arrives outside of the blocked state also causes deletion of the first matching syncpoint in the list; this case corresponds to the reverse case when the sender segment is ahead of the receiving segment in model time.
Deadlock
It is possible to cause deadlock with carelessly placed syncpoints. Suppose that segment A declares a syncpoint at 10s with segment B, but it will actually send a message only at 10.5s. If segment B does the same to segment A, a nice deadlock is created. OMNeT++ makes no effort to detect or prevent such deadlocks; it is entirely the simulation programmer's task to take care that deadlocks do not occur.
The pvmhosts file is used by PVM to describe what computers will participate in the virtual machine, where the executables (in our case, the OMNeT++ programs) are located on each computer, what working directories should be set etc.
It is advisable to have a common, shared directory mounted on all participating hosts; this eliminates the tedious work of having to copy files to all hosts again and again.
If using OMNeT++, it is a good idea to write separate pvmhosts files for each simulation program. Since simulation programs are typically in separate directories, the pvmhosts file in each directory can name that directory as executables directory and working directory for each host. This way, there is no need to create soft links or explicitly name directories in the OMNeT++ ini files.
Each line in the pvmhosts file describes one host. An example line (this all should be a single line!):
whale ip=whale.hit.bme.hu lo=andras ??=/home/andras/pvm/pvm ep=/home/andras/omnetpp/projects/fddi wd=/home/andras/omnetpp/projects/fddi
To start PVM with this configuration:
cd ~/omnetpp/projects/fddi pvm pvmhosts
The on: phrases in the NED descriptions specify the logical machine(s) on which the module is run. The machine parameters are mapped to physical machines in the [Machines] section of the configuration file:
; file: omnetpp.ini ;... [Machines] node1 = whale.hit.bme.hu node4 = whale.hit.bme.hu node2 = puppis.hit.bme.hu node3 = dolphin.hit.bme.hu ;...
Slave processes can be configured in the [Slaves] section of the configuration file:
; file: omnetpp.ini [Slaves] write-slavelog= slavelog-file= module-messages= errmsgs-to-console= infomsgs-to-console= modmsgs-to-console=
Screen input/output of the slaves is re-routed to the console. However, any file I/O is done in the local file system of each host.
The user must have PVM installed on the hosts he is going to run segments on.
To set up a simulation for distributed execution, the user must:
The first machine is called "console" or "master", the others are called "slaves".
PVM programs in general are more difficult to get running than ordinary programs. Wrong settings in the PVM configuration files can cause various problems, for example. Also, parallel programs are a lot harder to test and debug.
What can you do if your distributed OMNeT++ simulation won't work?
Similarly to other parallel discrete event simulation methods, the model to be simulated - which is more or less a precise representation of a real system - is divided into segments, where the segments usually describe the behaviour of functional units of the real system. The communication of the segments can be represented by sending and receiving various messages. The simulators of the segments are executed by separate processors.
The communication of these segments is simulated with appropriate interfaces. The messages generated in a given segment and to be processed in a different segment are not transmitted there, but the output interfaces collect the statistical data of them. If the input interfaces generate messages for the segments according to the statistical characteristics of the messages collected by the proper output interfaces, the segments with their input- and output interfaces can be simulated separately, giving statistically correct results. The events in one segment have not the same effect in other segments as in the original model, so the results collected during the SSM are not exact. The precision depends on the segmentation, on the accuracy of statistics collection and regeneration, and on the frequency of the statistics exchange among the processors.
Segmentation
The segments of the simulator are executed by separate processors, they have their own, independent virtual times. Because the interactions among segments are performed by the statistical parameters of these interactions, the segmentation should be done so, that the overwhelming majority of the interactions should happen within the segments and not among them. This speeds up the so-called inter-segment transients and improves the accuracy as well.
Timing of statistics exchange
Asynchronous statistics exchange means, that whenever a statistical result collection in an output interface is ready, it is applied - after mapping and correction - in the proper input interface. This is clearly more efficient, than the so-called synchronous statistics exchange, which means, that we delay the application of collected values until all the output interfaces get ready with the result collection. Frequent statistics exchange makes the inter-segment transient faster, but the lower sample numbers makes the estimation - and the whole simulation - less precise.
To learn more about SSM, see [PON92] and [PON93].
OMNeT++ directly supports the implementation of statistical interface with the following classes:
cLongHistogram, cDoubleHistogram, cPSquare, cPar.
Consider the following diagram:
A simulation program contains the simulated network (with its simple and compound modules etc.), SIM, ENVIR and exactly one of CMDENV and TKENV. SIM contains the simulation class library and the simulation kernel. The model only interacts with SIM. ENVIR contains code that's common for all user interfaces, and provides infrastructure like ini file handling for them. main() is also in ENVIR. Specific user interface code is contained in CMDENV and TKENV. The above components are also physically separated: they are in separate source directories and form separate library files (libsim_std.a, libenvir.a etc.)
The simulation program may contain several linked-in model components: networks, simple module types, compound module types, channel types etc. Any network (but only one at a time) can be set up for simulation which has all necessary components linked in.
Embedding is a special issue. You probably do not want to keep the appearance of the simulation program, so you do not want Cmdenv and Tkenv. You may or may not want to keep ENVIR.
What you'll absolutely need for a simulation to run is the SIM package. You can keep ENVIR if its philosophy and the infrastructure it provides (omnetpp.ini, certain command-line options etc.) fit into your design. Then the embedding program will take the place of Cmdenv and Tkenv.
If ENVIR does not fit your needs (for example, you want the model parameters to come from a database not from omnetpp.ini), then you have replace it. Your ENVIR replacement (the embedding program, practically) must implement the cEnvir member functions from envir/cenvir.h, but you have full control over the simulation.
Normally, code that sets up a network or builds the internals of a compound module comes from compiled NED source. You may not like the restriction that your simulation program can only simulate networks whose setup code is linked in. No problem; your program can contain pieces of code like what is generated by nedc and then it can build any network whose components (primarily the simple modules) are linked in. It is even possible to write an integrated environment where you can put together a network using a graphical editor and right after that you can run it, without intervening NED compilation and linkage.
The source code for the simulation kernel of OMNeT++ and the library classes reside in the sim directory.
Almost all objects are derived from cObject which provides a common interface for them.
The cSimulation class stores a network and manages simulation. There is only one instance, a global object called simulation. The object has two basic roles:
Base class for module classes: cModule. Two derived classes: cCompoundModule, cSimpleModule. User simple modules are derived from cSimpleModule.
A cModule has: array of parameters, array of gates + member functions to set up and query parameters and gates.
cSimpleModule adds: put-aside queue, list of local objects + the virtual function activity() + member functions like send(), receive() etc.
Gates are represented by the cGate objects. Connections are not real objects: their attributes (delay, error, datarate) are managed by the connection's source gate.
There are global objects holding lists of components available in an OMNeT++ executable. These lists are:
| List object | Macro that creates a member.
Class of members | Function |
cHead networks; | Define_Network() cNetworkType | List of available networks. A cNetworkType object holds a pointer to a function that can build up the network. Define_Network() macros occur in the code generated by the NEDC compiler. |
cHead modtypes; | Define_Module(), Define_Module_Like(), cModuleType | List of available module types. A cModuleType object knows how to create a module of a specific type. If is compound, it holds a pointer to a function that can build up the inside. Usually, Define_Module() macros for compound modules occur in the code generated by the NEDC compiler; for simple modules, the Define_Module() lines are added by the user. |
cHead classes; | Register_Class() ClassRegister | List of available classes of which the user can create an instance. A cClassRegister object knows how to create an (empty) object of a specific class. The list is used by the createOne() function that can create an object of any (registered) type from a string containing the class name. (E.g. ptr = createOne( "cArray") creates an empty array.) createOne() is used by the PVM extension. Register_Class() macros are present in the simulation source files for existing classes; has to be written by the user for new classes. |
cHead functions; | Define_Function() cFunctionType | List of mathematical functions. A cFunctionType object holds a pointer to the function and knows how many arguments it takes. |
cHead linktypes; | Define_Link() cLinkType |
List of link types. A cLinkType object knows how to create cPar objects representing the delay, error and datarate attributes for a channel. Define_Link() macros occur in the code generated by the NEDC compiler, one for each channel definition. |
cHead locals; | - any object | This is only 'dummy' object, it stands for the current module's local object list |
cHead superhead; | - cHead | List of all other lists. |
The coroutine package is in fact two coroutine packages.
There is a platform-independent coroutine package that creates all coroutine stacks inside the main stack. It was taken from [KOF85]. It allocates stack by deep-deep recursions and then plays with setjmp() and longjmp() to switch from one another. Its drawback is that under 16-bit Intel platforms (DOS real mode and Win16), stack is limited to 64K which is not very much.
The other package allocates stack by malloc() and uses a short assembly code to initialize it for the first use. Then it also uses setjmp() and longjmp(). This is implemented under DOS + BC3.1, and also RISC6000 where the original setjmp() / longjmp() behaved in an unfriendly way and the portable coroutine package could not be used.
The coroutines are represented by the cCoroutine class. cSimpleModule has cCoroutine as one a base class.
Ownership: Exclusive right and duty to delete the child objects. Ownership works through cObj's ownerp/prevp/nextp and firstchildp/lastchildp pointers.
'Contains' relationship: Only for container classes, e. g. cArray or cQueue. Keeping track of contained objects works with another mechanism, not the previously mentioned ptrs. (E.g., cArray uses a vector, cQueue uses a separate list).
The two mechanisms are independent.
What cObject does:
Rules for derived classes:
Rules for container objects derived from cObject:
The class cHead is special case: it behaves as a container, displaying objects it owns as contents.
The source code for the user interface of OMNeT++ resides in the envir directory (common part) and in the cmdenv, tkenv directories.
The classes in the user interface are not derived from cObject, they are completely separated form the simulation kernel.
The main() function of OMNeT++ simply sets up the user interface and runs it. Actual simulation is done in cEnvir::run() (see later).
The cEnvir class has only one instance, a global object called ev:
cEnvir ev;
cEnvir basically is only an interface, its member functions hardly contain any code. cEnvir maintains a pointer to a dynamically allocated simulation application object (derived from TOmnetApp, see later) which does all actual work.
cEnvir member functions deal with four basic tasks:
The base class for simulation application is TOmnetApp. Specific user interfaces such as TCmdenv, TOmnetTkApp are derived from TOmnetApp.
TOmnetApp's member functions are almost all virtual.
TOmnetApp's data members:
Concrete simulation applications:
TBD
OPNETTM (from MIL3 Inc.) is a state-of-the art commercial simulation program for the modeling of communication systems. OPNET is designed to enable full-detail modeling: every tool is given to implement nonstandard protocols or behaviour.
A quote from the OPNET brochure:
" OPNET presents an advanced graphical user interface that supports multi-windowing, makes use of menus and icons, and runs under X Windows. Supported platforms include popular engineering workstations from SUN, DEC, HP and Silicon Graphics. (Windows NT version also exists.)
Graphical object-oriented editors for defining topologies and architectures directly parallel actual systems, allowing an intuitive mapping between a system and its model. OPNET's hierarchical approach simplifies the specification and representation of large and complex systems.
The process editor provides a powerful and flexible language to design models of protocols, resources, applications, algorithms, queuing policies, and other processes. Specification is performed in the Proto-C language, which combines a graphical state-transition diagram approach with a library of more than 300 communication- and simulation-specific functions. The full generality and power of the C language is also available.
OPNET simulations generate user-selected performance and behavioral data. Simulation results can be plotted as time series graphs, scatter plots, histograms, and probability functions. Standard statistics and confidence intervals are easily generated and additional insight can be obtained by applying mathematical operators to the collected data.
OPNET provides an advanced animation capability for visualising simulation events. Both automatic and user-customised animations can be displayed interactively during or after a simulation. Animations can depict messages flowing between objects, control flow in a process, paths of mobile nodes, and dynamic values such as queue size or resource status.
OPNET provides open system features including: interfaces to standard languages; the ability to take advantage of third-party libraries; an application program interface; access to databases and data files such as those generated by network analysers; and PostScript and TIFF export for desktop publishing. OPNET users are guided by a comprehensive documentation set and are backed by outstanding technical support."
OPNET is very well designed and built commercial simulation software. The author of OMNeT++ has worked for the Hungarian distributor of OPNET for over three years and he has gained significant experience with the software. He has taken part in several computer network simulation projects for major Hungarian companies and also delivered OPNET training. He has also written simulation models for a VSAT system in OPNET.
Following is a comparison of the features that concern general-purpose computer systems simulation (and are not specific to computer network simulation) and that are present both in OMNeT++ and OPNET.
Model hierarchy levels
| OPNET | OMNeT++ |
| network level (subnetwork nesting possible)
node level (no nesting) process level (no nesting) | arbitrary levels of submodule nesting |
Topology description method
OPNET provides two tools for defining module topology: graphical
editors to design network and node level models, and EMA (External
Model Access), an API for building model files from C programs.
These tools correspond to OMNeT++'s tools in the following way:
| OPNET | OMNeT++ | |
| Graphical | graphical editor within the IDE | graphical editor: GNED |
| High-level | - | NED language |
| Low-level | EMA | C++ output of NED compilation |
There is no high-level textual model description in OPNET (like NED is in OMNeT++). This means that one has either to use the graphical editor or write lengthy C code using the EMA API.
The OPNET graphical model editor can only create fixed (non-parameterized) topologies.
There's a significant difference between how EMA and OMNeT++'s NED are used. OPNET's EMA generates model files. EMA applications are standalone programs: one writes the EMA C code, compiles and runs it, and the EMA executable will generate a model file that can be read into the graphical editor or loaded by simulation programs. EMA cannot be used from within a simulation program. In contrast, the compiled NED code of OMNeT++ becomes part of the simulation program and it builds the model without having to run external programs; this means that you can have a single simulation executable that can be used to perform simulation studies on networks with different topologies.
Module parameters
| OPNET | OMNeT++ | |
| Expressions | no expressions are allowed: only literals or exact copy of another parameter | arbitrary expressions using other parameters |
| Parameter passing | by value | parameters can be passed by value or by reference, and be changed during simulation |
| Usage | by process models only | by process models; also to define flexible topologies |
In OPNET, module parameter values can be passed only "as is".
Packet streams or gates
| OPNET | OMNeT++ | |
| Identification | Packet streams are numbered from 0; no names can be assigned. | Gates are identified by names. Gate vectors are supported.
In the code, gates can be referenced by ID for greater speed. |
| Directionality | Packet streams are uni-directional. | Gates are uni-directional. |
Flexible topologies
| OPNET | OMNeT++ |
| not really supported* | in the NED file, parameters can define submodule types, count of submodules, gates and describe connections |
* If really necessary, it can be done through C programming (writing EMA code) and running external program to create a separate model file for each case.
Tracing, animation and interactive simulation
| OPNET | OMNeT++ | |
| Tracing and debugging | powerful command line debugger (ODB) | separate window for each module's output, single-steps, run until, inspectors, snapshot, etc. (Tkenv) |
| Animation | mostly used in record/ playback mode;
animation spec. must be given in advance (via anim. probes) | interactive execution with message-flow animation, statistics animation etc. (Tkenv) |
| Interactive simulation | not supported | strongly supported via object inspectors and watches. (Tkenv) |
Random numbers
| OPNET | OMNeT++ | |
| Distributions provided | many built-in distributions (through algorithms) | four built-in distributions, as C functions |
| Additional distributions | through histograms | as C functions (algorithms); or through histograms |
| Random number generation | one random number generator, no support for seed selection | several independent random number generators;
tool to support selecting good seed values |
OPNET has many built-in distributions implemented with algorithms (C functions). Additional distributions are supported as histograms. There is only one common source of random numbers. OPNET has no aid for selecting seed values that produce long non-overlapping random number sequences.
OMNeT++, only four basic distributions are provided. They are implemented as C functions. Additional distributions can be added by the user, and they are treated exactly in the same way as built-in ones. Defining and using distributions in histogram form is also supported. OMNeT++ provides several random number generators, and also a tool for selecting good seed values.
Process description method
| OPNET | OMNeT++ | |
| Method | finite state machine (graphical spec. only) | both process-style (coroutine-based) and finite state machine (textual spec. only) |
Direct (non-scheduled) process interaction
| OPNET | OMNeT++ | |
| Method | "forced interrupt" | member function call of other module |
Dynamic module creation
| OPNET | OMNeT++ | |
| What can be created | only processes within an existing module | simple modules;
connections; compound modules with arbitrarily complex, parameterized topologies |
Object-oriented concepts
| OPNET | OMNeT++ | |
| Language | C | C++ |
| Objects | C API functions operating on object-like data structures;
no support for inheritance*, polymorphism or the like | full flexibility of C++: inheritance, polymorphism etc; built-in object-oriented mechanisms |
* The graphical user interface of OPNET (from version 3.0) contains an "inheritance mechanism" for models. This is no real inheritance in the object-oriented sense because it just means that parameter values can be changed or fixed down, parameters renamed, merged etc. There is no mention about changing the behaviour of a module (that is, anything like C++'s virtual functions).
Statistics collection and run-time analysis
| OPNET | OMNeT++ |
| writing observations to output file; "probes" to select statistics to be collected;
only off-line analysis (analysis of output files) is supported | writing observations to output files (roughly equivalent to OPNET's solution); run-time processing: basic measures (mean etc); distribution estimation with histograms; quantiles (P2 algorithm); support for detecting the end of the transient period and sufficient result accuracy |
Parallel execution
| OPNET | OMNeT++ |
| not supported | supported over PVM; arbitrary synchronization can be used |
Openness
| OPNET | OMNeT++ | |
| Input file formats | binary model files*; textual parameter files | text files |
| Output file formats | binary files** | text files |
| Availability of source | not available (only the source of the shipped models is available) | available |
| Embedding simulations into other software product | not supported and also not possible (the main() function cannot be supplied by the user etc.) | supported.
Embedding application becomes a new "user interface" based on Envir (1); or embedding application replaces Envir (2). |
* Can be read and analyzed by EMA programs.
** Can be exported to text files from the main OPNET program.
This section is intended to help OPNET users learn OMNeT++ faster.
| OPNET | OMNeT++ |
| network, subnetwork, node | Compound modules |
| module, process | An OMNeT++ simple module corresponds to an OPNET module with its process. |
| interrupts, invocations, states | When using handleMessage(): interrupt = event, invocation = call to handleMessage(), state = FSM state or the value of the state vars stored in the class
When using modules with activity(), this means a little different way of thinking from OPNET's. In OMNeT++, you write a simple module as you would write an operating system process or a thread, thus there's no need to distinguish 'states' or speak about 'invocations'. Within the simulation kernel, an 'invocation' corresponds to a transferTo(module) call. An OMNeT++ module accepts messages (and simulation time advances) within receive…(…) calls; wait() is just a scheduleAt() followed by a receive(). An OPNET interrupt is the event being processed. In this sense, OMNeT++ messages returned by receive() correspond to OPNET interrupts. |
| endsim interrupt | The finish() virtual member functions of the simple modules are called at the end of the simulation run. You can redefine finish() to write statistics etc. |
op_ima_obj_attr_get(…) |
foo = par("foo");
foo = module->par("foo");
|
op_ima_sim_attr_get(…) | There are no simulation attributes. You can use the parameters of the top-level module instead:
foo = simulation.systemModule()->par("foo");
|
op_prg_odb_print_minor(…) op_prg_odb_print_major(…) | ev << "hello!" << endl; ev.printf(…); |
op_sim_end(…) |
simulation.error("Your fault! error 0",ec);
|
op_subq_....() | Create a queue object and then manipulate it with its member functions.cQueue queue; queue.insert( msg ); if (!queue.empty()) msg = queue.pop(); |
List op_prg_list_...() | cLinkedList list; list.insert( ptr ); if (!list.empty()) ptr = list.pop(); |
Topology op_rte_...() | The cTopology class offers similar functionality, and you can expect greater speed than with OPNET's routing functions. |
Packet op_pk_create(…) op_pk_destroy(…) | Use the cMessage class.
cMessage *msg = new cMessage; delete msg; |
packet fields op_pk_nfd_set(…) op_pk_nfd_get_(…) op_pk_fd_set(…) op_pk_fd_get(…) | Message parameters. A parameter has both name and index.
msg->par("foo") = foo;
msg->addPar("new-foo") = foo;
int foo = msg->par("foo");
int fooindex = msg->parList().find("foo");
msg->par(fooindex) = foo;
|
| packet field modelled size | Message parameters do not have associated modelled bit sizes. Message length can be used instead.
msg->addPar("dest_addr") = dest_addr;
msg->addLength( 32 );
|
| packet formats | There are no explicit packet formats in OMNeT++. However, you can write function to create messages with specific fields and length:
cMessage *createEthernetFrame()
{
cMessage *msg = new cMessage;
msg->setKind(PACKET);
msg->addPar("source");
msg->addPar("destination");
msg->addPar("protocol");
msg->setLength( 8*16 );
return msg;
}
|
| packet encapsulation | As in OPNET, message parameters can be assigned object pointers, thus also message pointers. However, there is also direct support encapsulation: msg->encapsulate(innermsg) innermsg = msg->encapsulatedMsg(); innermsg = msg->decapsulate(); |
ICI |
ICIs are also represented by cMessage objects, naturally with zero length.
If it is important to distinguish between packets and ICIs, you can use the message kind field: #define PACKET 0 #define ICI 1 cMessage *pk = new cMessage; pk->setKind(PACKET); cMessage *ici = new cMessage; ici->setKind(ICI); |
| ICI formats | See packet formats. |
| ICI attributes | See packet fields. |
| packet and ICI in the same interrupt | You can use encapsulation. At the sender side:cMessage *ici, *pk; ici->encapsulate(pk); send(ici,"out-gate"); The receiver side: ici = receive(); pk = ici->decapsulate(); |
op_pk_send(…) | send( msg, "out-gate"); send( msg, "gate-vector", index); send( msg, gate_id ); |
op_pk_send_delayed(…) | sendDelayed(…) |
op_pk_deliver(…) | sendDirect(…) |
op_pk_schedule_self(…) | scheduleAt( simTime()+timeout, msg ); |
op_ev_cancel(…) | cancelEvent( msg ); |
op_dist_load(…) op_dist_outcome(…) | To generate random numbers from analytical distributions, use:uniform(…) intuniform(…) exponential(…) normal(…) truncnormal(…) For custom distributions you can use the histogram classes. Histograms can load distribution data from file.
cDoubleHistogram hist;
FILE *f = fopen("distribution.dat");
hist.loadFromFile( f );
fclose(f);
double rnd = hist.random();
|
| output vectors | The cOutVector class can be used.
cOutVector eed("End-to-end delay");
double d = msg->creationTime() - simTime();
eed.record( d );
|
| output scalars | Output scalar file exists. You can write into it with recordScalar():
recordScalar("average delay", avg_delay);
|
op_topo_parent() | cModule *parent = parentModule(); |
op_topo_child_…(…) | cSubModuleIterator |
op_topo_.._assoc_(…) | gate(i)/gate(name), gate->toGate()/fromGate() gate->destinationGate()/sourceGate() gate->ownerModule() |
op_pro_create(…) | See dynamic module creation. Note that this is a more powerful tool than OPNET's dynamic processes in that you can also create compound modules. |
Prohandle | Module ID. Given the module pointer, you can obtain module ID byint id = mod->id(); And you can obtain module pointer from the ID: cModule *mod = simulation.module(id); An invalid ID is negative. |
op_pro_invoke(…) | Dynamically created modules do not need to be invoked, they live their own life. To dispatch messages to them, you can use sendDirect(…) |
op_pro_destroy(…) op_pro_destroy( self ) | deleteModule( module ); deleteModule(); |
| module memory, parent-to-child memory, argument memory to dynamic processes | Parent module can set pointers (void* data members) in the dynamically created module object any time, thus also right after creating it ( parent-to-child memory), right before sending a packet to it ( argument memory), and the pointer can refer to memory managed by the parent module ( module memory).
An example for argument memory. Suppose the child module class has a public data member named argmem:
class ChildModule : public cSimpleModule {
…
public:
void *argmem;
…
};
The parent module code would be: childmod->argmem = argument_memory_ptr; sendDirect( msg, childmod, 0.0, "in" ); Child module code would be: msg = receive(); argument_memory_ptr = argmem; |
op_pro_valid(…) | Given the module id:int valid = (id>=0) && simulation.exist(id); |
| Environment files | Configuration files. Default is omnetpp.ini. Multiple ini files and ini file inclusion are also supported. |
| Process Editor | Your favourite text editor. Or vi :-). |
| Network Editor, Node Editor | Any editor to write NED files.
GNED. Not very sophisticated yet though. |
| Simulation Tool | Use the [Run 1], [Run 2] etc. sections in omnetpp.ini do describe several runs with different parameters.
To create loops on different variables, you can use a shell script that creates a short ini file with the variable parameters, and include that file in omnetpp.ini. |
| probes, Probe Editor | From the ini file, you can turn on/off cOutVector objects individually as well as assign result collection interval to them. |
| Analysis Tool | Plove |
| EMA | Where you would normally use EMA, OMNeT++ NED files with parameterized topology are often enough.
Otherwise, you have two choices: a) write a program to generate NED files. Text-processing languages like perl and awk are great tools for that. b) write the network-building code in C++. You can look at the output of nedc for some idea how to do it. |
PARSEC is a very successful simulation language, with strong support for parallel simulation. PARSEC bears some similarity to OMNeT++ in that it is also based on threads/coroutines. The language and the software has been developed at the Parallel Computing Laboratory of the University of California Los Angeles (UCLA), under the leadership of Prof. Rajive Bagrodia. PARSEC has been used in a number of simulation projects, for example in simulation of mobile radio networks in a military environment.
It is best to quote the PARSEC User Manual, Release 1.1 (August 1998):
PARSEC (for PARallel Simulation Environment for Complex programs) is a C-based discrete event simulation language. It adopts the process interaction approach to discrete event simulation. An object (also referred to as a physical process) or a set of objects in the physical system is represented by a logical process [a thread - roughly equivalent to an OMNeT++ simple module --Andras]. Interactions among physical processes (events) are modeled by timestamped message exchanges among the corresponding logical processes.
One of the important distinguishing features of PARSEC is its ability to execute a discrete-event simulation model using several different asynchronous parallel simulation protocols on a variety of parallel architectures. [...] Thus, with few modifications, a PARSEC program may be executed using the traditional sequential (Global Event List) simulation protocol or one of many parallel [...] protocols. [In reality, PARSEC currently supports only conservative PDES but not any optimistic protocol --Andras]
In addition, PARSEC provides powerful message receiving constructs that result in shorter and more natural simulation programs. [...]
The PARSEC language has been derived from the Maisie language, but with several improvements, both in the syntax of the language and in its execution environment.
The PARSEC web site is at http://pcl.cs.ucla.edu/. It is very impressive.
When you download and install the PARSEC distribution, basically you find:
This shows that PARSEC is strictly a simulation (and parallel programming) language which is restricted to the area of entities, messages, and the tasks centered around message sending and receiving. It is difficult to compare to OMNeT++ which is more of a complete simulation environment. In OMNeT++, the simulation library alone covers much more functionality than the whole PARSEC. PARSEC has a very efficient support for parallel execution.
In the further sections, I will give a brief overview of Parsec, with special attention to the strong and weak sides compared to OMNeT++.
NOTE: I am doing my best, however, THIS IS NOT A REPLACEMENT FOR THE PARSEC MANUAL. It is possible that there are errors in this chapter. If you want to have a closer look at Parsec, you must download and read the official Parsec manual from UCLA.
As it was mentioned earlier, we can only compare Parsec against the OMNeT++ simulation kernel, that is, the functionality around send...(), receive...(), wait() and the coroutines (the activity() function), because Parsec simply doesn't offer much more than that.
The first look
The PARSEC is a programming language based on C (not C++!). PARSEC programs, in addition to normal C code, contain special syntactic constructs, so they do not compile as C. You have to invoke the PARSEC compiler (pcc) which parses the whole program and translates it into true C code.
The main advantage of this solution is that the PARSEC language is clean and really elegant.
Let us see a bit of PARSEC code:
#include <stdio.h>
...
message job {
int id;
int count;
};
message add_to_your_sorc {
ename id;
};
...
entity driver(int argc, char **argv) {
...
}
Two constructs stand out at once: message and entity. The message constructs define message types, and are translated to C structs. Entities correspond to OMNeT++'s simple modules, and the entity body is equivalent to the activity() function. ename is a data type that holds entity references.
Restricted use of C++
Because the parser inside pcc is written for C, it is not possible to use any C++ constructs. This means you can NOT use:
Today, when practically no one is programming in pure C, this is a very serious limitation. Again, the limitation comes from pcc itself: it doesn't understand any C++. It is irrelevant whether you use a C or C++ compiler to compile pcc's output.
The driver entity -- assembling the model by hand
The driver entity is special; in a way it is similar to the C main() function. PARSEC starts the simulation by creating and running a driver entity. The main task of the driver is to create all other entities in the simulation and provide them with information they need (parameters values, etc). The latter is done by sending to the entities messages with the necessary parameters.
PARSEC does not have a high-level topology description language like NED in OMNeT++; instead, the driver entity is hand-coded most of the time. (There was no mention of tools that could generate the driver entity based on some higher-level description.).
OMNeT++ compound modules have no equivalent in PARSEC. All entities are at the same level, there's no way to express hierarchy.
PARSEC has no notion of module gates, and there are no connections (in the OMNeT++ sense) among the entities. This means that when sending messages, the receiving entity must be explicitly named. Since the program contains no explicit topology information, an entity initially has no information about its communication partners (it knows no enames except its own). The usual practice is that the driver entity sends the necessary enames in an initialization message to each entity. (For illustration, see the add_to_your_sorc message type from the above code fragment. The message name itself is quite descriptive...)
The consequence of the lack of compound modules and module gates is that it is a complicated and tricky task to set up networks with but the most trivial topology. It is also very difficult to write reusable simulation components without well-defined interfaces and structuring (compound modules).
Problems with splitting up the entity body
During programming, the code of an entity may become so large that it is no longer feasible to keep it within a single function body. In OMNeT++ you can solve the problem by distributing the simple module class's activity() code into new member functions which are called from activity(), and moving the some local variables of activity() into the module class so that they can also be accessed by the new member functions.
The above approach doesn't work in Parsec, because Parsec is C-based and entities are not C++ classes. Of course one may call ordinary C functions from the entity body, but the necessary parameters must be passed in the argument list (or as pointers to data structures in the entity).
Another solution in Parsec is to use a construct called friend functions (not to be confused with C++ friend functions). Parsec's friend functions may access the local variables of the entity (quite strange in C, but much like an inner procedure in Pascal...). However, the Parsec documentation does not recommend using friend functions (they are slow); it says they are provided for Maisie compatibility.
Message sending
Messages can be sent to other entities with the send construct:
send message to dest-entity after delay;
For example, creating a new message of type Request with the parameters 10 and self (the current entity) and sending it to entity2 entity after a delay looks like this:
send Request{10,self} to entity2 after 5;
This Parsec construct is totally equivalent in functionality to OMNeT++'s sendDirect( message, delay, dest-module [,dest-gate]) call. Since Parsec has no equivalent of OMNeT++ gates, OMNeT++'s other send() functions which send messages through a gate are not present in Parsec.
Message receiving constructs
Parsec entities accept messages with the receive construct. Receive has many forms: the elegance and power of the Parsec language stems from the receive construct. Some illustrative examples:
receive (Request req) {
...
} or receive (Release rel) {
...
} or timeout in (5) { /*"in": timeout with high priority*/
...
}
It is possible to add guards to the receive branches:
receive (Request req) when (req.units<=units) {
...
} or timeout after (5) { /*"after": timeout with low priority*/
...
}
These constructs have to be explicitly programmed in OMNeT++ using while loops with receive() calls and if/switch statements in its body. The reason OMNeT++ doesn't have this sort of syntax and functionality is that it is impossible to express with plain C/C++: one cannot avoid the need for a special precompiler. Having to use a precompiler, however, causes some inconvenience during program development, and in practice, there isn't as much need for this sort of complex receive constructs that would justify making it mandatory to use a precompiler for every source file.
One may wonder what happens to the messages which have arrived already but have not been accepted by the entity yet because they had no matching receive branch. Parsec stores those messages in what it calls the message buffer of the entity. Parsec's message buffer is practically the same as the put-aside queue in OMNeT++.
Guards may contain the special expressions qhead(msgtype), qempty(msgtype), qlength(msgtype) which refer to the messages in the message buffer. The programmer perceives as if each message type had a separate message buffer:
receive (Request req) when (qhead(Request).units<=units); receive (Request req) when (qempty(Release) && req.units<=units);
Note that the qhead(), qempty() and qlength() operations seem to be all you can do with the message buffer, while in OMNeT++ you have free access to the put-aside queue through the cQueue member functions.
Parsec also has a hold statement which is functionally equivalent to OMNeT++'s wait():
hold(5);
Cancelling messages
Parsec has no support for cancelling messages, that is, there is no equivalent to OMNeT++'s cancelEvent(). The Parsec team recommends different workarounds like keeping a list of valid timers and checking messages against that.
A posting from the Parsec bulletin board:
Re: Cancelling messages in PARSEC Posted by Rich from al-bundy.cs.ucla.edu on November 30, 1999. Chano Gomez posted: > I would like to be able to cancel a message that has been already sent, > but that hasn't been received by the recipient (because the message > has been sent using the "send message to entity after x" clause). > > Is this possible in PARSEC? No. > I'd like to do this for activating and cancelling timers. There are other ways to implement timers in Parsec, particularly if the timers are sent to "self." One way is to keep a list of timeout values and set the X in "timeout in (X)" to the minimum in the list. (i.e. use timeout, don't send messages) To cancel, simply remove from the list. Another way is to send to self, but keep a list of cancelled messages (in a circular queue, for example). When the message is received, check to see if it was cancelled, and if so, discard it. Rich
Simulation clock
The PARSEC simulation clock is of integer type: optionally, unsigned int or long long (long long is not a standard ANSI C/C++ type). OMNeT++ uses double.
It is probably application-specific which is the better choice, but in the case of a large simulation model put together from components written with different time granularity in mind, double seems a better choice because it is relative insensitive to the choice of the time unit.
Thread/coroutine handling
Although the Parsec documentation says nothing, symbol names is the Parsec runtime library give the impression that the thread/coroutine implementation is quite similar in OMNeT++ and the single-processor implementation of Parsec. On Unix systems, both simulators use a portable coroutine library (proposed by Stig Kofoed, based on setjmp/longjmp). On NT, Parsec seemingly uses the thread implementation of the underlying operating system.
One advantage of OMNeT++ is that it can tell you how much stack space a module uses in fact (see the stackUsage() function), so you can optimize the stack size. Parsec apparently doesn't have this feature.
The worst problem with coroutines/threads is that if you create too many of them, you'll need a hell lot of memory. With the current engineering workstations, it is practically impossible to create more than a few times ten thousand entities in Parsec (this is requires a few hundred megabytes of memory).
OMNeT++ has basically the same problem, but it gives you two escapes:
Parsec has no such escapes, so the problem may hit you hard.
Comparison of PARSEC and OMNeT++ as parallel simulation tools
A fundamental difference between Parsec and OMNeT++ is that Parser supports parallel simulation (i.e. simulation on a multiprocessor) while OMNeT++ supports distributed simulation (over several hosts).
This difference accounts for the usage differences. While Parsec's parallel simulation support is almost transparent to the user, OMNeT++'s distributed simulations are inherently more difficult to set up and manage (you have to install PVM, etc).
Parsec supports conservative PDES; OMNeT++ supports conservative PDES and Statistical Synchronization.
| Feature | OMNeT++ | PARSEC |
| Programs, components: | ||
| graphical model editor | GNED | - |
| result analysis/plotting | Plove | - |
| interactive execution, tracing | Tkenv | - |
| parameter file | omnetpp.ini | - |
| random numbers support | seedtool | - |
| Model structure | ||
| encapsulation/grouping | compound modules | - |
| connections | yes (optionally: delay, data rate, bit error rate) | - |
| topology description | via NED, nedc | - (manually from the driver entity) |
| Simulation methodology | ||
| Precompiler | - (no need, code is standard C++) | pcc (Parsec compiler) |
| C++ support | based on C++ | - (language based on C) |
| alternative to coroutines/threads | handleMessage() | - |
| complex message receiving constructs | - (timeout only) | yes: filter by message type, timeout, guards, etc. |
| message types | via subclassing cMessage or via cMessage + pars | via the message construct |
| module gates, sending via gates | yes | - (direct sending only) |
| module parameters | yes | - |
| dynamic module (entity) creation | yes (also compound modules) | yes |
| Simulation library | ||
| statistics/histogram classes | yes (cStdDev, 3 histogram classes, P2 , k-split) | - |
| routing support | yes (cTopology) | - |
| FSM support | yes (FSM macros) | - |
| support for output files | yes (cOutVector, recordScalar(),...) | - |
| container classes | yes (cQueue, cArray,...) | - |
| Parallel execution | ||
| conservative | yes | yes (much more elaborate than in OMNeT++) |
| optimistic | - | - |
| statistical synchronization | explicit support | possible, but no support |
| PARSEC | OMNeT++ |
| entity | simple module (cSimpleModule) |
| message | message (cMessage, cPacket,...) |
| message buffer of the entity | put-aside queue |
| send message to entity after delay | sendDirect( message, delay, module [,destgate]) |
| send message to self after delay | scheduleAt( message, simTime()+delay)) |
| n/a
(Parsec has no equivalent of OMNeT++ gates) | send(message,gate)
sendDelayed(message,gate,delay) |
| hold( delay ) | wait( delay ) |
| receive (msgtype msg) { ... } | msg = receive() |
| receive (msgtype msg) { ... }
or timeout after (delay) { ... } | msg = receive( delay ) |
| more complex receive constructs | while { msg=receive(); if (...) ... } |
The NED language, the network topology description language of OMNeT++ will be given using the extended BNF notation.
Space, horizontal tab and new line characters counts as delimiters, so one or more of them is required between two elements of the description which would otherwise be unseparable. '//' (two slashes) may be used to write comments that last to the end of the line. The language only distinguishes between lower and upper case letters in names, but not in keywords.
In this description, the {xxx...} notation stands for one or more xxx's separated with spaces, tabs or new line characters, and {xxx,,,} stands for one or more xxx's, separated with a comma and (optionally) spaces, tabs or new line characters.
For ease of reading, in some cases we use textual definitions. The networkdescription symbol is the sentence symbol of the grammar.
notation meaning
[a] 0 or 1 time a
{a} a
{a,,,} 1 or more times a, separated by commas
{a...} 1 or more times a, separated by spaces
a|b a or b
'a' the character a
bold keyword
italic identifier
networkdescription ::=
{ definition ... }
definition ::=
include
| channeldefinition
| simpledefinition
| moduledefinition
| networkdefinition
include ::=
INCLUDE { fileName ,,, } ;
channeldefinition ::=
CHANNEL channeltype
[ DELAY numericvalue ]
[ ERROR numericvalue ]
[ DATARATE numericvalue ] ******
ENDCHANNEL
simpledefinition ::=
SIMPLE simplemoduletype
[ machineblock ]
[ paramblock ]
[ gateblock ]
ENDSIMPLE [ simplemoduletype ]
moduledefinition ::=
MODULE compoundmoduletype
[ machineblock* ]
[ paramblock ]
[ gateblock ]
[ submodblock ]
[ connblock ]
ENDSIMPLE [ compoundmoduletype ]
moduletype ::=
simplemoduletype | compoundmoduletype
machineblock ::=
MACHINES: { machine ,,, } ;
paramblock ::=
PARAMETERS: { parameter ,,, } ;
parameter ::=
parametername
| parametername : CONST [ NUMERIC ]
| parametername : STRING
| parametername : BOOL
| parametername : CHAR
| parametername : ANYTYPE
gateblock ::=
GATES:
[ IN: { gate ,,, } ; ]
[ OUT: { gate ,,, } ; ]
gate ::=
gatename [ '[]' ]
submodblock ::=
SUBMODULES: { submodule ... }
submodule ::=
{ submodulename : moduletype [ vector ]
[ on_block* ... ]
[ substparamblock ... ]
[ gatesizeblock ... ] }
| { submodulename : parametername [ vector ] LIKE moduletype
[ on_block* ... ]
[ substparamblock ... ]
[ gatesizeblock ... ] }
on_block* ::=
ON [ IF expression ]: { on_machine ,,, } ;
substparamblock ::=
PARAMETERS [ IF expression ]:
{ substparamname = substparamvalue,,, } ;
substparamvalue ::=
( [ ANCESTOR ] [ REF ] name )
| parexpression
gatesizeblock ::=
GATESIZES [ IF expression ]:
{ gatename vector ,,, } ;
connblock ::=
CONNECTIONS [ NOCHECK ]: { connection ,,, } ;
connection ::=
normalconnection | loopconnection
loopconnection ::=
FOR { index ... } DO
{ normalconnection ,,, } ;
ENDFOR
index ::=
indexvariable '=' expression "..." expression
normalconnection ::=
{ gate { --> | <-- } gate [ IF expression ]}
| {gate --> channel --> gate [ IF expression ]}
| {gate <-- channel <-- gate [ IF expression ]}
channel ::=
channeltype
| [ DELAY expression ] [ ERROR expression ] [ DATARATE expression ]
******
gate ::=
[ modulename [vector] . ] gatename [vector]
networkdefinition ::=
NETWORK networkname : moduletype
[ on_block ]
[ substparamblock ]
ENDNETWORK
vector ::= '[' expression ']'
parexpression ::=
expression | otherconstvalue
expression ::=
expression + expression
| expression - expression
| expression * expression
| expression / expression
| expression 0.000000e+000xpression
| expression ^ expression
| expression == expression
| expression != expression
| expression < expression
| expression <= expression
| expression > expression
| expression >= expression
| expression ? expression : expression
| expression AND expression
| expression OR expression
| NOT expression
| '(' expression ')'
| functionname '(' [ expression ,,, ] ')' ***
| - expression
| numconstvalue
| inputvalue
| [ ANCESTOR ] [ REF ] parametername
| SIZEOF**** '(' gatename ')'
| INDEX*****
numconstvalue ::=
integerconstant | realconstant | timeconstant
otherconstvalue ::=
'characterconstant'
| "stringconstant"
| TRUE
| FALSE
inputvalue ::=
INPUT '(' default , "prompt-string" ')'
default ::=
expression | otherconstvalue
* used with distributed execution
** used with the statistical synchronization method
*** max. three arguments. The function name must be declared in the C++ sources with the Define_Function macro.
**** Size of a vector gate.
***** Index in submodule vector.
****** Can appear in any order.
This is a fairly complete reference of the object library of OMNeT++. Many details are not covered in this documentation. Only public member functions of the classes are documented. Some of the classes which are not visible to the user have been omitted, and of the classes that are documented, some member functions are very briefly or not at all described. However, I hope this material can be used as a handy reference.
Header file: defs.h and util.h (sim directory)
NO(cXX) NOOBJ
The NO(classname) macro can be used for NULL pointers of a specific type. For most classes in the simulation library, there is also a one-word shorthand for NO(classname): NOOBJ, NOPAR, NOMSG etc.
simtime_t MAXTIME
Type to measure model time (#defined to double); its maximum value.
PI
The value of , 6.14159… .
typedef int (*CompareFunc)(cObject *, cObject *);
Type of function used in a cQueue object to compare items. See documentation on cQueue for more information.
typedef bool (*ForeachFunc)(cObject *,bool);
Type of function to be passed to forEach(). See cObject::forEach() for more information.
typedef double (*MathFunc)(...); typedef double (*MathFuncNoArg)(); typedef double (*MathFunc1Arg)(double); typedef double (*MathFunc2Args)(double,double); typedef double (*MathFunc3Args)(double,double,double);
Used by cPar and sXElem.
typedef void (*VoidDelFunc)(void *); typedef void *(*VoidDupFunc)(void *);
Memory management functions for void* pointers. Used by cLinkedList and cPar.
char *simtimeToStr(simtime_t t, char *dest=NULL);
Converts simtime_t (a double) to textual form that contains hours, minutes, second, milliseconds and microseconds like this: "00h 34m 23s 130ms 230us".
If you do not provide a destination buffer for simtimeToStr(), it will place the result into a static buffer that is overwritten with each call.
simtime_t strToSimtime( const char *str );
Converts a textually given simulation time (e.g. "30s 152ms") to simtime_t. If the string cannot be entirely interpreted, -1 is returned.
simtime_t strToSimtime0( const char *&str );
Similar to strToSimtime(); it can be used if the time string is a substring in a larger string. It only goes as far as it can in str, then sets the str pointer to the first character that could not be interpreted as part of the time string, and returns the value. It never returns -1; if nothing at the beginning of the string looked like simulation time, it returns 0.
These functions replace some of the <string.h> functions. The difference is that they also accept NULL pointers and treat them as pointers to an empty string "" and use operator new instead of malloc(). Use these functions instead of original <string.h> versions!
char *opp_strdup(const char *);
Duplicates the string. If the pointer passed is NULL or points to a null string (""), NULL is returned.
char *opp_strcpy(char *,const char *);
Same as the standard strcpy() function, except that NULL pointers in the second argument are treated like pointers to a null string ("").
int opp_strcmp(const char *, const char *);
Same as the standard strcmp() function, except that NULL pointers are treated like pointers to a null string ("").
bool opp_strmatch(const char *, const char *);
Returns true if the two strings are identical up to the length of the shorter one. NULL pointers are treated like pointers to a null string ("").
const char *correct(const char *);
Returns the pointer passed as argument unchanged, or, if it was NULL, returns a pointer to a null string ("").
char *opp_concat(const char *s1, const char *s2, const char *s3=NULL, const char *s4=NULL);
Concatenates 2, 3, or 4 strings and places the result into a static buffer and returns the buffer's pointer. The result's length shouldn't exceed 255 characters.
char *indexedname(char *buf, const char *name, int index);
Creates a string like "component[35]" into buf, the first argument.
OMNeT++ has a built-in pseudorandom number generator that gives long int (32-bit) values.
Range: 1 ... 231-2
Period length: 231-2
Method: x=(x * 75) mod (231-1)
To check if it works correctly: starting with x[0]=1, then x[10000]=1,043,618,065 must hold. Required hardware: exactly 32-bit integer arithmetics.
Source: Raj Jain: The Art of Computer Systems Performance Analysis (John Wiley & Sons, 1991), pages 441-444, 455.
You can use the generator through the following functions:
void opp_randomize();
Sets the random number generator's seed to a random value.
long randseed(long seed=0);
Sets the random number generator's seed to the given value. Zero cannot be used as seed.
long intrand();
Returns an integer random number in the range [0,INTRAND_MAX-1].
long intrand(long r);
Returns an integer random number in the range [0, r-1] (works if r << INTRAND_MAX)
double dblrand();
Returns a random number in the range [0.0, 1.0).
double uniform(double a, double b);
Uniform distribution in the range [a,b).
double intuniform(double a, double b);
An integer with uniform distribution in the range [a,b]. Note that the function can return also a or b.
double exponential(double mean);
Exponential distribution (with parameter 1/mean).
double normal(double mean, double variance);
Normal distribution with mean and variance given.
double truncnormal(double mean, double variance);
Normal distribution truncated to nonnegative values; this is done by discarding negative values until a nonnegative comes.
Min(a,b); Max(a,b);
Macros returning the minimum/maximum of two values.
bool equal(double a, double b, double limit);
Tests equality of two doubles, with the given precision. Returns true if fabs(a-b)<limit.
bool lowmemory();
Returns true if free space on the heap is getting too low.
ASSERT(condition);
Makes sure the condition holds (evaluates to nonzero). Otherwise, it stops the simulation with a detailed error message. Use ASSERT() to verify your program works correctly and what you assume about program state is really true.
class opp_string;
A value-added version of char*. Has only one data member, a char* pointer. Added value is automatic allocation/deallocation (through opp_strdup/delete). A string object has its own (opp_strdup()'ped) copy of the string.
Recommended use: as class member, where otherwise the class members would have to call opp_strdup() and delete for the char* member.
It is intentionally kept very simple. Usable wherever char* is needed. Its only data member is the char* pointer so string is even usable with printf() which doesn't do conversion to char*. To keep this very valuable property, don't add any other data members or any virtual functions.(Virtual functions would add a virtual table pointer to objects.)
Example usage:
opp_string a, string b("foo");
a = "bar";
a = b;
const char *s = a;
printf("string: `'\n", a );
Member functions:
opp_string(); opp_string(const char *s); ~opp_string();
String creation, destruction.
operator const char *();
Returns pointer to the string.
const char *operator=(const char *s);
Deletes the old value and opp_strdup()'s the new value to create the object's own copy.
Header file: ctypes.h (sim directory)
Network(NAME,SETUPFUNCTION)
Network declaration macro, it can be found in code generated by the NEDC compiler. The use of this macro allows the creation of a network when only its name is available as a string. (Typically, the name of the network to be simulated is read from the configuration file.)
The macro expands to the definition of a cNetworkType object.
Define_Link(NAME,DELAY,ERROR,DATARATE)
Link type definition. The macro expands to the definition of a cLinkType object; the last three arguments are pointers to functions which dynamically create cPar objects an return their pointers.
Define_Function(FUNCTION,ARGCOUNT)
Registers a mathematical function that takes 0, 1, 2 or 3 double arguments and returns a double. The use of this macro allows the function to be used in expressions inside NED network descriptions.
The commonly used <math.h> functions have Define_Function() lines in the OMNeT++ simulation kernel sources.
Register_Class(CLASSNAME)
Registers a class for use with the createOne() function:
cObject *createOne(const char *classname);
This function creates an object of the type given by the string argument. createOne() is used by the distributed execution part of OMNeT++, for unpacking objects from a buffer that was sent over from another machine/processor.
Each class that should be able to travel between machines/processors should be registered with the Register_Class macro in one of the C++ source files.
(The Register_Class macro expands to the definition of a cClassRegister object.)
Interface(CLASSNAME) Gate(NAME,TYPE) Parameter(NAME,TYPES) Machine(NAME) End
Simple module declaration macro. Gate types can be 'Input' or 'Output', parameter types can be 'Anytype' or 'Numeric'.
Module_Class_Members(CLASSNAME,BASECLASS,STACKSIZE)
This macro facilitates the declaration of a simple module type's class. The macro is used like this:
class CLASSNAME : public cSimpleModule
{
Module_Class_Members(CLASSNAME,cSimpleModule,8192)
virtual void activity();
};
The macro expands to the definition of member functions which the user does not need to worry about: constructors, destructor, className() function etc. The user can derive the new class from an existing simple module class (not only cSimpleModule), add new data members and add/redefine member functions as needed.
Define_Module(CLASSNAME) Define_Module_Like(CLASSNAME, INTERFACENAME)
The use of this macro allows the creation of a module (simple or compound) when only its name is available as a string. The macro expands to the definition a cModuleType object (and some functions it needs).
The NEDC compiler generates Define_Module() lines for all compound modules. However, it is the user's responsibility to put Define_Module() lines for all simple module types into one of the C++ sources.
Header file: cobject.h (sim directory)
cObject is the base class for almost all classes in the OMNeT++ library. cObject provides a name member (a dynamically allocated string) and a number of virtual functions. These functions:
Read this section carefully if you plan to create new classes.
Construction, destruction, copying
cObject(cObject& obj);
Copy constructor. In derived classes, it is usually implemented as {operator=(obj);}.
cObject(const char *s, cObject *h);
Creates a cObject with the given name. The owner will be the h object (if the pointer is not NULL), that is, the constructor contains a setOwner( h ) call.
virtual ~cObject();
Virtual destructor. Deletes the name and notifies the user interface that the object has been destructed.
virtual cObject *dup();
Duplicates the object and returns a pointer to the new one. In derived classes, it is usually implemented as {return new cObject(*this);}.
cObject& operator=(cObject& o);
The assignment operator. Most derived classes contain a cSomeClass& cSomeClass:: operator=(cSomeClass&) function. The assignment operators do not copy the object name. If you want to do so, you can copy it be hand: setName(o.name());
void *operator new(size_t m);
cObject's operator new does more than the global new(). It cooperates with cObject's constructor to determine the storage class of the object (static, auto or dynamic).
char storage();
Returns the storage class of the object. The return value is one of the characters S/A/D which stand for static, auto and dynamic, respectively.
void destruct();
Direct call to the virtual destructor.
Name member
virtual void setName(const char *s);
Sets object's name. The object creates its own copy of the string. NULL pointer may also be passed.
const char *name();
Returns pointer to the object's name. The function never returns NULL; rather, it returns ptr to "".
virtual const char *fullName();
To be redefined in descendants. E.g., see cModule::fullName().
virtual const char *fullPath(int l);
Returns the name of the object with its place in the object ownership hierarchy ("net.comp.modem[5].baud-rate"). See also cModule::fullPath().
bool isName(const char *s);
Returns true if the object's name is identical with the string passed.
User interface information functions
virtual const char *className();
Returns a pointer to the class name string, "cObject". In derived classes, usual implementation is {return "classname";}.
virtual void info(char *buf);
Copies a short description of the object into buf. This function is used by the graphical user interface (TkEnv). See also Functions supporting snapshots.
virtual const char *inspectorFactoryName();
Returns the name of the class which can create inspector windows for objects of this class (e.g. in Tkenv).
Ownership control
virtual cObject *defaultOwner();
This function should return a pointer to the default owner of the object. The function is used by the drop() member function, redefined in cObject-descendant container classes.
cObject *owner();
Returns pointer to the owner of the object.
void setOwner(cObject *newowner);
Sets the owner pointer of the object. See documentation of cHead for more information.
Operations to be used by container classes derived from cObject
void takeOwnership(bool tk); bool takeOwnership();
Sets/returns the flag which determines whether the container object should automatically take ownership of the objects that are inserted into it.
void take(cObject *obj);
The function called by the container object when it takes ownership of the obj object that is inserted into it. Implementation: obj->setOwner( this ).
void drop(cObject *obj);
The function called by the container object when obj
is removed from the container -- releases the ownership of the
object and hands it over to its default owner. Implementation:
obj->setOwner( obj->defaultOwner() );
void free(cObject *obj);
The function is called when the container object has to delete
the contained object obj. It the object was dynamically
allocated (by new), it is deleted, otherwise (e.g., if
it is a global or a local variable) it is just removed from the
ownership hierarchy. Implementation:
{if(obj->storage()=='D') delete obj; else obj->setOwner(NULL);}
Load/store operations
The load/store operations are used when running OMNeT++ on multiple processors. They are used to exchange data between two instances of OMNeT++ running in different processes (and possibly different processors), using the statistical synchronization method. Currently, this is implemented over PVM (Parallel Virtual Machine version 3).
virtual int netPack(const char * type=NULL); virtual int netUnpack();
These functions are expected to be redefined in all derived objects. In OMNeT++'s PVM interface, they call pvm_pkint(), pvm_upkint() etc. functions.
Functions supporting snapshots
virtual void writeTo(ostream& os);
This function is called internally by cSimpleModule::snapshot(). It writes out info about the object into the stream. Relies on writeContents(). writeTo() does not need to be redefined.
virtual void writeContents(ostream& os);
This function is called by internally writeTo(). It is expected to write textual information about the object and other objects it contains to the stream. The default version (cObject::writeContents()) uses forEach to call info() for contained objects. Redefine as needed.
Other functions
static int cmpbyname(cObject *one,cObject *other);
This function compares to objects by name. It can be used in a priority queue (class cQueue) as a sorting criterion.
The forEach() mechanism
virtual void forEach(ForeachFunc f);
Makes sense with container objects derived from cObject. Calls the f function recursively for each object contained in this object.
The forEach() mechanism
The forEach() mechanism implemented in OMNeT++ is very special and slightly odd. The passed function is called for each object twice: once on entering and once on leaving the object. In addition, after the first ('entering') call to the function, it signals with its return value whether it wants to go deeper in the contained objects or not.
Functions passed to forEach() will use static variables to store other necessary information. (Yes, this limits their recursive use :-( ).
forEach() takes a function do_fn (of ForeachFunc type) with 2 arguments: a cObject* and a bool. First, forEach() should call do_fn(this,true) to inform the function about entering the object. Then, if this call returned true, it must call forEach(do_fn) for every contained object. Finally, it must call do_fn(this,false) to let do_fn know that there's no more contained object.
Functions using forEach() work in the following way: they call do_fn(NULL, false, <additional args>) to initialize the static variables inside the function with the additional args passed. Then they call forEach(do_fn) for the given object. Finally, read the results by calling do_fn(NULL, false, <additional args>), where additional args can be pointers where the result should be stored. ForeachFuncs mustn't call themselves recursively!
(I know this all is kind of weird, but I wrote it a long ago. Changing it now would take quite some work, and after all, it works..)
Member functions based on forEach()
cObject *findObject(const char *s, bool deep=true);
Finds the object with the given name in a container object and returns a pointer to it or NULL if the object hasn't been found. If deep is false, only objects directly contained will be searched, otherwise the function searches the whole subtree for the object.
bool ishe_there(cObject *obj);
Returns true if the given object obj is contained in this object's subtree.
Header file: cmodule.h (sim directory)
There are three module classes. cModule is the common base class for all module classes. It contains data and functions to manage the module hierarchy in a network and handles parameters and gates. cModule is used only as a base class: no instance of cModule is ever created. cCompoundModule is derived from cModule and represents compound modules in networks. cCompoundmod has no added functionality over cModule. cSimpleModule is also a descendant of cModule and is a base class for all simple module classes.
cModule contains data and functions to manage the module hierarchy in a network. It also stores and controls module parameters and gates.
Construction, destruction, copying
The user normally doesn't create new modules directly, so these member functions are not documented here.
Redefined virtual functions
virtual const char *className()
Returns pointer to the class name string, "cModule".
virtual const char *fullName();
Returns full name of the module in a static buffer, in the form "name" or "name[index]".
virtual const char *fullPath(int l);
Returns full path from the system module to the module in a static buffer, in the form "system_module.submodule[1].subsubmodule.name[index]".
virtual void info(char *buf); virtual const char *inspectorFactoryName();
Redefined.
Member functions
bool isSimple() = 0;
Pure virtual function. It is redefined in cSimpleModule to return true and in cCompoundModule to return false.
bool isOnLocalMachine();
Used with parallel execution: determines if the module is on the local machine. See the user manual for more info.
int id();
Returns the index of the module in the module vector (cSimulation simulation).
cModule* parentModule();
Returns reference to the module's parent.
int index();
Returns the index of the module if it is multiple, otherwise 0.
int size();
Returns the size of the multiple module, or 1.
Module gates
int gates();
Returns total number of module gates.
cGate& gate(int g);
Returns reference to the gate identified with its index g. Returns *NULL if the gate doesn't exist.
cGate& gate(const char *s,int sn=0);
Returns reference to the gate specified by name and index (if multiple gate). Returns *NULL if the gate doesn't exist.
int findGate(const char *s, int sn=0);
Returns index of the gate specified by name and index (if multiple gate). Returns -1 if the gate doesn't exist.
Module parameters
int params();
Returns total number of the module's parameters.
cPar& par(int p);
Returns reference to the module parameter identified with its index p. Returns *NULL if the object doesn't exist.
cPar& par(const char *name);
Returns reference to the module parameter specified with its name. Returns *NULL if the object doesn't exist.
int findPar(const char *s);
Returns index of the module parameter specified with its name. Returns -1 if the object doesn't exist.
cPar& ancestorPar(const char *name);
Searches for the parameter in the parent modules, up to the system module. It the parameter is not found, an error message is generated.
Initialize and finish functions
void initialize(); void initialize(int stage); int numInitStages(); void finish();
TBD
Display Strings
void setDisplayString(int type, const char *dispstr,
bool immediate=true);
TBD
const char *displayString(int type);
TBD
Warning messages
bool warnings();
Warning messages can be enabled/disabled individually for each module. This function returns the warning status for this module: true=enabled, false=disabled.
void setWarnings(bool wr);
Enables or disables warnings for this module: true=enable, false=disable.
Other functions
bool checkInternalConnections();
For compound modules, it checks if all gates are connected inside the module (it returns true if they are OK); for simple modules, it returns true.
virtual void scheduleStart(simtime_t t) = 0;
Pure virtual function; it is redefined in both cCompoundModule and cSimpleModule. It creates starting message for dynamically created module (or recursively for its submodules). See the user manual for explanation how to use dynamically created modules.
virtual void deleteModule() = 0;
Pure virtual function; it is redefined in both cCompoundModule and cSimpleModule. Deletes a dynamically created module and recursively all its submodules.
int findSubmodule(const char *submodname, int idx=-1);
Finds an immediate submodule with the given name and (optional) index, and returns its module ID. If the submodule was not found, returns -1.
cModule *submodule(const char *submodname, int idx=-1);
Finds an immediate submodule with the given name and (optional) index, and returns its pointer. If the submodule was not found, returns NULL.
cModule *moduleByRelativePath(const char *path);
Finds a submodule potentially several levels deeper, given with its relative path from this module. (The path is a string of module full names separated by dots). If the submodule was not found, returns NULL.
Redefined member functions
bool isSimple();
Returns false.
virtual void scheduleStart(simtime_t t);
Calls scheduleStart() recursively for all its (immediate) submodules. This is used with dynamically created modules.
virtual void deleteModule();
Calls deleteModule() for all its submodules and then deletes itself.
cSimpleModule is derived from cModule and is a base class for all simple module classes. Most important, cSimpleModule has a virtual member function called activity() that is to be redefined in descendants -- this is the module function. All basic functions associated with the simulation such as sending and receiving messages are implemented as cSimpleMod's member functions. The module functions are run as coroutines during simulation. Coroutines are brought to cSimpleModule from another base class called cCoroutine.
Construction, destruction, copying
The user normally doesn't create new modules directly, so these member functions are not documented here.
Redefined member functions
virtual const char *className()
Returns pointer to the class name string, "cSimpleModule".
virtual void info(char *buf); virtual const char *inspectorFactoryName();
Redefined.
bool isSimple();
Returns true.
virtual void scheduleStart(simtime_t t);
Creates a starting message for the module.
virtual void deleteModule();
Deletes a dynamically created module.
Public data members
cQueue putAsideQueue;
Represents the put-aside queue (where the simulator puts unexpected messages that arrive to the module). The putaside-queue is configured to sort the messages by arrival time and priority, like the event queue does. You can freely use the contents of the putaside-queue; the simulator kernel does not do anything with it, it only inserts messages. See class cQueue and the functions cSimpleModule::wait(), cSimpleModule::receiveNewOn(), cSimpleModule::receive() for more information.
cHead locals;
List of local variables of module function. You do not normally need to care about this object.
cHead members;
Data members of derived classes. If you derive a class from cSimpleModule and add object members, you should call object->setOwner( members ) for each object you add.
The module function
virtual void activity();
Contains the module function. It is empty in cSimpleModule and should be redefined in descendants.
Getting the current simulation time
simtime_t simTime();
Returns the current simulation time (that is, the arrival time of the last message returned by a receiveNew() call).
Sending messages
int send(cMessage *msg, int g);
Sends a message through the gate given with its ID.
int send(cMessage *msg, const char *s, int sn=0);
Sends a message through the gate given with its name and index (if multiple gate).
int send(cMessage *msg, cGate *outputgate);
Sends a message through the gate given with its pointer.
int sendDelayed(cMessage *msg, double delay, int g);
Sends a message through the gate given with its index as if it was sent delay seconds later.
int sendDelayed(cMessage *msg, double delay, const char *s, int sn=0);
Sends a message through the gate given with its name and index (if multiple gate) as if it was sent delay seconds later.
int sendDelayed(cMessage *msg, double delay, cGate *outputgate);
Sends a message through the gate given with its pointer as if it was sent delay seconds later.
int sendDirect(cMessage *msg, double delay, cGate *inputgate);
TBD (also to include other sendDirect() funcs!)
int scheduleAt(simtime_t t, cMessage& msg);
Inserts the given message into the Future Event Set and schedules it to be returned at time t. This function can be used to implement timers.
cMessage *cancelEvent(cMessage *msg);
Removes the given message from the message queue. The message needs to have been sent using the scheduleAt() function. This function can be used to cancel a timer implemented with scheduleAt().
Receiving new messages
cMessage *receiveNew(simtime_t t=MAXTIME);
Removes the next message from the event queue and returns a pointer to it. If there is no message in the event queue, the function waits with t timeout until a message will be available. If the timeout expires and there is still no message in the queue, the function returns NULL.
cMessage *receiveNewOn(const char *s, int sn=0, simtime_t t=MAXTIME);
The same as receiveNew(), except that it returns the next message that arrives on the gate specified with its name and index. All messages received meanwhile are inserted into the put-aside queue. If the timeout expires and there is still no such message in the queue, the function returns NULL.
In order to process messages that may have been put in the put-aside queue, the user is expected to call receive() or receiveOn(), or to examine the put-aside queue directly sometime.
cMessage *receiveNewOn(int g, simtime_t t=MAXTIME);
Same as the previous function except that the gate must be specified with its index in the gate array. Using this function instead the previous one may speed up the simulation if the function is called frequently.
bool isThereMessage();
Tells if the next message in the event queue is for the same module and has the same arrival time. (Returns true only if two or more messages arrived to the module at the same time.)
Getting messages from the put-aside queue or the event queue
cMessage *receive(simtime_t t=MAXTIME);
Returns the first message from the put-aside queue or, if it is empty, calls receiveNew() to return a message from the event queue with the given timeout. Note that the arrival time of the message returned by receive() can be earlier than the current simulation time.
cMessage *receiveOn(const char *s, int sn=0, simtime_t t=MAXTIME);
Scans the put-aside queue for the first message that has arrived on the gate specified with its name and index. If there is no such message in the put-aside queue, calls receiveNew() to return a message from the event queue with the given timeout. Note that the arrival time of the message returned by receive() can be earlier than the current simulation time.
cMessage *receiveOn(int g, simtime_t t=MAXTIME);
Same as the previous function except that the gate must be specified with its index in the gate array. Using this function instead the previous one may speed up the simulation if the function is called frequently.
Waiting
void wait(simtime_t time);
Wait for given interval. The messages received meanwhile are inserted into the put-aside queue.
Stopping the module or the simulation
void end();
Ends the run of the simple module. The simulation is not stopped (unless this is the last running module.)
void endSimulation();
Causes the whole simulation to stop.
Help for tracing and debugging
bool snapshot(cObject *obj = &simulation, const char *label = NULL);
To be called from module functions. Outputs textual information about all objects of the simulation (including the objects created in module functions by the user!) into the snapshot file. The output is detailed enough to be used for debugging the simulation: by regularly calling snapshot(), one can trace how the values of variables, objects changed over the simulation. The arguments: label is a string that will appear in the output file; obj is the object whose inside is of interest. By default, the whole simulation (all modules etc) will be written out.
Tkenv also supports making snapshots manually, from menu.
See also class cWatch and the WATCH() macro.
void setPhase(const char *s);
Sets the phase string (the function creates a copy of the string). The string can be displayed in user interfaces which support tracing / debugging (currently only Tkenv) and the string can contain information that tells the user what the module is currently doing.
const char *phase();
Returns pointer to the current phase string.
void pause(const char *s);
If the user interface supports step-by-step execution, one can stop execution at each receive() call of the module function and examine the objects, variables, etc. If the state of simulation between receive() calls is also of interest, one can use pause() calls. The string argument (if given) sets the phase string, so pause("here") is the same as setPhase("here"); pause().
void breakpoint(const char *label);
Specifies a breakpoint. During simulation, if execution gets to a breakpoint() call (and breakpoints are active etc.), the simulation will be stopped, if the user interface can handle breakpoints.
Non-typed heap allocation/deallocation for module functions
void *memAlloc(size_t m);
Dynamic memory allocation. This function should be used instead of the global ::malloc() from inside the module function (activity()), if deallocation by the simple module constructor is not provided.
Dynamic allocations are discouraged in general unless you put the pointer into the class declaration of the simple module class and provide a proper destructor. Or, you can use container classes (cArray, cQueue)!
void memFree(void *&p);
Frees a memory block reserved with the malloc() described above and NULLs the pointer.
Header file: cmessage.h, cpacket.h (sim directory)
cMessage is the message class in OMNeT++. cMessage can be assigned a name (a property inherited from cObject) and it has other attributes, including message kind, length, priority, error flag and time stamp.
After being sent through a channel, cMessage also remembers the sending and delivery times and its source module. cMessage holds a cArray which means that a cMessage can be attached any number of objects. These objects will typically be of cPar type, but other types are also possible.
Construction, destruction, copying
cMessage(cMessage& msg);
Copy constructor, creates an exact copy of the argument msg.
cMessage(const char *name=NULL, int k=0, int len=1, int pri=0, bool err=false);
Constructor. It accepts name, message kind, length, priority and error flag as arguments.
virtual cObject *dup() cMessage& operator=(cMessage& msg);
Duplication and the assignment operator work all right with cMessage.
Redefined virtual functions
virtual const char *className()
Returns pointer to the class name string, "cMessage".
Message properties
void setKind(int k);
Sets message kind. The message kind member is not used by OMNeT++, it can be used freely by the user.
void setPriority(int p);
Sets message priority. The priority member is used when the simulator inserts messages in the message queue (FES) to order messages with identical arrival time values.
void setLength(long l);
Sets message length. When the message is transmitted through a channel, its error flag will be set with a probability depending on the length of the message and the channel's bit error rate.
void setError(bool err);
Directly sets the message's error flag.
void setTimestamp();
Sets the message's time stamp to the current simulation time.
void setTimestamp(simtime_t stamp);
Directly sets the message's time stamp.
int kind();
Returns message kind. The message kind member is not used by OMNeT++, it can be used freely by the user.
int priority();
Returns message priority. The priority member is not used by OMNeT++, it can be used freely by the user.
long length();
Returns message length.
bool hasBitError();
Returns true if error flag is set, false otherwise.
simtime_t timestamp();
Returns the message's time stamp.
Message encapsulation
void encapsulate(cMessage *msg);
Encapsulates msg in the message. msg->length() will be added to the length of the message.
cMessage *decapsulate();
Decapsulates a message from the message object. The length of the message will be decreased accordingly, except if it was zero. If the length would become negative, an error occurs.
cMessage *encapsulatedMsg();
Returns a pointer to the encapsulated message, or NULL.
Info about sending/scheduling
int senderModuleId();
Returns sender module's index in the module vector or -1 if the message hasn't been sent/scheduled yet.
int senderGateId();
Returns index of gate sent through in the sender module or -1 if the message hasn't been sent/scheduled yet.
int arrivalModuleId();
Returns receiver module's index in the module vector or -1 if the message hasn't been sent/scheduled yet.
int arrivalGateId();
Returns index of gate the message arrived on in the sender module or -1 if the message hasn't sent/scheduled yet.
cGate *senderGate() cGate *arrivalGate()
Returns pointers to the gate from which the message was sent and on which gate it arrived. A NULL pointer is returned for new (unsent) messages and messages sent via scheduleAt().
simtime_t creationTime();
Returns time when the message was created.
simtime_t sendingTime();
Returns time when the message was sent/scheduled or 0 if the message hasn't been sent yet.
simtime_t arrivalTime();
Returns time when the message has arrived or 0 if the message hasn't been sent/scheduled yet.
bool arrivedOn(int g);
Return true if the message has arrived through gate g.
bool arrivedOn(const char *s, int g=0);
Return true if the message has arrived through the gate given with its name and index (if multiple gate).
Parameters and attached objects
cArray& parList();
Returns the cArray member of the message which holds the parameters and other attached objects. Parameters can be inserted, retrieved, looked up or deleted through cArray's member functions.
cPar& addPar(cPar& p); cPar& addPar(const char *name);
Convenience functions, add a parameter to the message's parameter list.
cPar& par(int index);
Convenience function, returns the indexth object in the message's parameter list, converting it to a cPar.
cPar& par(const char *name);
Convenience function, returns the object with the given name in the message's parameter list, converting it to a cPar.
int hasPar(const char *name);
Convenience function, returns true if there's an object with the given name in the message's parameter list..
int findPar(const char *name);
Convenience function, returns the index of the parameter with the given name in the message's parameter list, or -1 if it could not be found.
Miscellaneous functions
static int cmpbydelivtime(cObject *one, cObject *other);
Static function that compares two messages by their delivery times, then by their priorities.
static int cmpbypriority(cObject *one, cObject *other);
Static function that compares two messages by their priority. It can be used to sort messages in a priority queue.
cPacket is not yet documented here. See the User Manual.
Header file: cmodule.h (sim directory)
cGate object are created and managed by modules; the user typically does not want to directly create or destroy cGate objects. However, they are important if a simple module algorithm needs to know about its surroundings.
Construction, destruction, copying
These functions are not documented here since they are considered internal to the simulation library.
Redefined virtual functions
virtual const char *className()
Returns pointer to the class name string, "cGate".
Gate identity
int size()
If the gate is in a gate array, returns the size of the vector; otherwise, it returns 1.
int index()
If the gate is in a gate array, returns the gate's position in it; otherwise, it returns 0.
void setIndex(int sn, int vs)
Specifies that the gate is at index sn in a gate array of size vs. This function should not be directly called by the user.
char type()
Returns the gate's type: 'I' for input and 'O' for output.
cModule *ownerModule()
Returns a pointer to the owner module of the gate.
int id()
Returns the position of the gate in the vector of all gates of the module.
void setOwnerModule(cModule *m, int g)
Specifies that the gate is owned by module m, and it is at index g in the gate vector. This function should not be directly called by the user.
Link parameters
cLinkType *link()
Returns the link type of the gate, if it has one.
cPar *delay() cPar *error() cPar *datarate()
Return pointers to the delay, bit error rate and datarate parameters of the link. Links are one-directional; these parameters are only stored at their starting side.
void setLink(cLinkType *l)
Sets the parameters of the link to those specified by the link type.
void setDelay(cPar *p) void setError(cPar *p) void setDataRate(cPar *p)
Set the parameters of the link. Ownership of cPar objects are handled according to the ownership flag (that is set by takeOwnership()).
Topology
bool isConnected()
Returns true if the gate is connected.
cGate *fromGate() cGate *toGate()
For a compound module gate, it returns the previous and the next gate in the series of connections (the route) that contains this gate. For simple module gates, only one of the functions will return non-NULL value.
void setFrom(cGate *g) void setTo(cGate *g)
Redirect gates. This function will rarely be needed; unless maybe for dynamically created modules.
cGate *sourceGate() cGate *destinationGate()
Return the ultimate source and destination of the series of connections (the route) that contains this gate.
int routeContains(cModule *m, int g=-1)
Determines if a given module is in the route that this gate is in.
bool isRouteOK()
Returns true if the route that this gate is in is complete; i.e., if it starts and arrives at a simple module.
void deliver(cMessage *msg)
This function is called internally by the send() functions to deliver the message to its destination.
Header file: cpar.h (sim directory)
Parameter classes are designed to hold a value. Many types are available:
For all types, an input flag can be set. In this case, the user will be asked to enter the value when the object's value is first used. The prompt string can also be specified for cPar. If no prompt string is given, the object's name will be displayed as prompt text.
Construction, destruction, copying
cPar(cPar& other);
Copy constructor, creates an exact copy of the argument.
cPar(const char *name=NULL);
Constructor, creates a cPar with the given name and long ('L') as default type.
cPar(const char *name, cPar& other);
Constructor, creates a copy of the second argument with another name.
virtual cObject *dup()
Duplicates the object and returns a pointer to the new one.
cPar& operator=(cPar& otherpar);
The assignment operator works with cPar objects.
Redefined virtual functions
virtual const char *className()
Returns pointer to the class name string, "cPar".
Member functions handling type, input flag and prompt
char type();
Returns type character. If the "real" type is 'I', it returns the type of the object it is redirected to (for example, 'D', 'L', etc.)
const char *prompt();
Returns the prompt text or NULL.
void setPrompt(const char *s);
Sets the prompt text.
void setInput(bool ip);
Sets (ip=true) or clears (ip=false) the input flag.
bool isInput();
Returns true if the parameter is of input type (the input flag is set).
bool changed();
Returns true if the value has changed since the last changed() call.
Conversion from/to text
virtual bool setFromText(const char *text, char type);
This function tries to interpret the argument text as a type typed value. type=='?' means that the type is to be auto-selected. On success, cPar is updated with the new value and true is returned, otherwise the function returns false. No error message is generated.
virtual void getAsText(char *buf, int maxlen);
Places the value in text format it into buffer buf which is maxlen characters long.
Setting the value
cPar& setStringValue(char tp, const char *s);
Sets the value stored in the object.
TBD other set...Value() functions
Retrieving the value
const char *stringValue();
Returns value as char*. Only for string (S) type.
bool boolBalue();
Returns value as bool. Converts from types Boolean (B) and long (L).
long longValue();
Returns value as long. Converts from types long (L), double (D), Boolean (B), function (F), distribution (T) and expression (X).
double doubleValue();
Returns value as double. Converts from types long (L), double (D), function (F), Boolean (B), distribution (T) and expression (X).
cObject *objectValue();
Returns value as pointer to cObject. Type must be pointer (O).
void *pointerValue();
Returns value as pointer to cObject. Type must be pointer (P).
Other functions
bool isNumeric();
Returns true if the stored value is of a numeric type.
cPar& read();
Reads the object value from the ini file or from the user.
void convertToConst();
Replaces the object value with its evaluation (a double). Implemented as something like setValue('D', this->doubleValue()).
void configPointer(VoidDelFunc delfunc, VoidDupFunc dupfunc, size_t size = 0);
Configures memory management for the void* pointer ('P') type. Similar to cLinkedList's configPointer() function.
| delete func. | dupl.func. | itemsize | behaviour |
| NULL | NULL | 0 | Pointer is treated as mere pointer - no memory management. Duplication copies the pointer, and deletion does nothing. |
| NULL | NULL | >0 size | Plain memory management. Duplication is done with new char[size]+memcpy(), and deletion is done via delete. |
| NULL or user's delete func. | user's dupfunc. | indifferent | Sophisticated memory management. Duplication is done by calling the user-supplied duplication function, which should do the allocation and the appropriate copying. Deletion is done by calling the user-supplied delete function, or the delete operator if it is not supplied. |
static int cmpbyvalue(cObject *one, cObject *other);
Compares two cPars by their value if they are numeric. This function can be used to sort cPar objects in a priority queue.
Handling redirection
cPar& setRedirection(cPar *par);
TBD
bool isRedirected();
TBD
cPar *redirection();
Returns NULL if the cPar's value is not redirected to another cPar; otherwise it returns the pointer of that cPar. This function is the only way to determine if an object is redirected or not (type() returns the type of the other cPar: 'D', 'L' etc).
void cancelRedirection();
Break the redirection. The new type will be long ('L').
Assignment and conversion operators
This set of overloaded operators provides assignment and conversion for fundamental data types.
TBD update!
cPar& operator=(char c);
Equivalent to setValue('C',(int)c).
cPar& operator=(unsigned char c);
Equivalent to setValue('C',(int)c).
cPar& operator=(const char *s);
Equivalent to setValue('S',s).
cPar& operator=(int i);
Equivalent to setValue('L',(long)i).
cPar& operator=(unsigned int i);
Equivalent to setValue('L',(long)i).
cPar& operator=(long l);
Equivalent to setValue('L',l).
cPar& operator=(unsigned long l);
Equivalent to setValue('L',(long)l).
cPar& operator=(double d);
Equivalent to setValue('D',d).
cPar& operator=(long double d);
Equivalent to setValue('D',(double)d).
cPar& operator=(cObject *obj);
Equivalent to setValue('P',obj).
operator char*();
Equivalent to stringValue().
operator int();
Equivalent to longValue().
operator unsigned int();
Equivalent to longValue().
operator long();
Equivalent to longValue().
operator unsigned long();
Equivalent to longValue().
operator double();
Equivalent to doubleValue().
operator long double();
Equivalent to doubleValue().
operator cObject *();
Equivalent to pointerValue().
The following operators are overloaded: <, >, <=, >=, +, -, * and /. These operators help using cPar objects in arithmetic expressions with the same syntax as internal numeric types. Two different sets of overloaded arithmetic operators are provided, mutually excluding the other. One works through converting the cPar to double, and the other through converting to long. The double version is the default, but you can choose the other set by defining LONG_CPAR_OPERATIONS before including cpar.h. If neither set is needed, define NO_CPAR_OPERATIONS!
If the value of the cPar is of expression type, the expression must be converted to reversed Polish form. The reversed Polish form expression is stored in a vector of sXElem structures. sXElem is not a descendant of cObject.
void operator=(int _i); void operator=(long _l); void operator=(double _d);
Effect during evaluation of the expression: pushes the given number (which is converted to double) on the evaluation stack.
void operator=(cPar *_v);
Effect during evaluation of the expression: takes the value of the cPar object (a double) and pushes the value on the evaluation stack. The cPar is an "external" one: its ownership does not change. This is how NED-language REF parameters in expressions are handled.
void operator=(cPar& _r);
Effect during evaluation of the expression: takes the value of the cPar object (a double) and pushes the value on the evaluation stack. The cPar which evaluates this expression will copy the cPar for itself.
void operator=(char _op);
The argument can be:
| add, subtract, multiply, divide | |
| modulo, power of | |
| equal, not equal | |
| greater, greater or equal | |
| less, less or equal | |
| inline if (C language's (cond ? a : b) |
Effect during evaluation of the expression: two items (or three, with '?') are popped out of the stack, the given operator is applied to them and the result is pushed back on the stack.
void operator=(MathFuncNoArg f); void operator=(MathFunc1Arg f); void operator=(MathFunc2Args f); void operator=(MathFunc3Args f);
The argument can be a pointer to a function that takes (0, 1, 2, or 3) double arguments and returns a double (e.g. sqrt()). Effect during evaluation of the expression: the given number of doubles are popped from the stack, the given function is called with them as arguments, and the return value is pushed back on the stack. See also the cFunctionType class and the Define_Function() macro.
The OMNeT++ functions generating random variables of different distributions can also be used in sXElem expressions.
Header files: carray.h, cqueue.h (sim directory)
There are four container classes: cArray and cQueue, cHead.
cArray is a container object that holds objects derived from cObject. cArray stores the pointers of the objects inserted instead of making copies. cArray works as an array, but if it gets full, it grows automatically by a specified delta. Ownership of contained objects (responsibility of deletion) can be specified per-object basis (see cObject::takeOwnership()). Default is that cArray takes the ownership of each object inserted (that is, takeOwnership(true)).
Construction, destruction, copying
cArray(cArray& list);
Copy constructor. Contained objects that are owned by cArray (that is, whose owner() is the cArray) will be duplicated so that the new cArray will have its own copy of them.
cArray(const char *name=NULL, int siz=0, int dt=10, cHead *h=&locals);
Constructor. It takes the object name, the initial vector size, delta and the pointer to the list it should join as argument.
virtual ~cArray();
Destructor. The contained objects that were owned by the container will be deleted.
virtual cObject *dup(); cArray& operator=(cArray& list);
Duplication and assignment work all right with cArray. Contained objects that are owned by cArray will be duplicated so that the new cArray will have its own copy of them.
Redefined virtual functions
virtual const char *className();
Returns pointer to the class name string,"cArray".
virtual void info(char *buf); virtual const char *inspectorFactoryName();
Redefined.
virtual void forEach(ForeachFunc f);
Calls the given function for each object contained.
Member functions:
int items();
Returns the index of last used position+1.
void clear();
As a result, the container will be empty. Contained objects that were owned by the container will be deleted.
int add(cObject *obj);
Inserts a new object into the array. Only the pointer of the object will be stored. The return value is the object's index in the array.
int addAt(int m, cObject *obj);
Inserts a new object into the array, at the given position. If the position is occupied, the function generates an error message.
int find(cObject *obj);
Searches the array for the pointer of the object passed and returns the index of the first match. If the object wasn't found, -1 is returned.
int find(const char *s);
Returns the index of the first item in the array that has the name pointed to by s (cObject::isName() is used.) If no such item was found, -1 is returned.
cObject *get(int m);
Returns reference to the mth object in the array or null pointer if the mth position is not used.
cObject *get(const char *s);
Returns reference to the first object in the array with name s or null reference (*NOOBJ) if no object with the given name was found.
cObject *operator[](int m); cObject *operator[](const char *s);
The same as get(int)/get(const char *). With the indexing operator, cArray can be used as a vector.
bool exist(int m); bool exist(const char *s);
Returns true if the value returned by get(int)/get(const char *) would not be null reference (*NOOBJ).
cObject *remove(int m); cObject *remove(const char *s); cObject *remove(cObject *p);
Removes the object given with its index/name/pointer from the container. (If the object was owned by the container, drop() is called.)
cQueue is a container class that can hold objects derived from cObject. cQueue acts as a priority queue. The user must provide a function that can compare two objects. If no such function is given, cQueue implements a FIFO. Order (ascending or descending) can be specified and will be interpreted as in the figure:
Ownership of contained objects (responsibility of deletion) can be specified per-object basis (see cObject::takeOwnership()). Default is that cQueue takes the ownership of each object inserted (that is, takeOwnership(true)).
The sorting function
int CompareFunc(cObject& a, cObject& b);
User-supplied sorting functions must have a declaration like the one shown above and they must return a negative value if a<b, 0 if a==b and a positive value if a>b.
Construction, destruction, copying
cQueue(cQueue& queue);
Copy constructor. Contained objects that are owned by the queue will be duplicated so that the new queue will have its own copy of them.
cQueue(const char *name=NULL, CompareFunc cmp=NULL, bool a=false);
Constructor. It accepts the object name, the address of the comparing function, the sorting order (ascending=true, descending=false) and the list the object will join.
virtual ~cQueue();
Destructor. Deletes all contained objects that were owned by it.
virtual cObject *dup(); cQueue& operator=(cQueue& queue);
Duplication and assignment work all right with cQueue. Contained objects that are owned by the queue will be duplicated so that the new queue will have its own copy of them.
Redefined virtual functions
virtual const char *className();
Returns pointer to the class name string,"cQueue".
virtual void info(char *buf); virtual const char *inspectorFactoryName();
Redefined.
virtual void forEach(ForeachFunc f);
Calls the given function for each object contained.
Member functions
void setup(CompareFunc cmp=NULL, bool a=false);
Changes the sort function and the sorting order. Doesn't re-sort the contents of the queue!
void clear();
As a result, the container will be empty. Contained objects that were owned by the queue will be deleted.
int length();
Returns the number of objects contained in the queue.
bool empty();
Returns true if the queue is empty.
void insert(cObject *obj);
Inserts the given object into the queue, maintaining the sorting order.
void insertBefore(cObject *where, cObject *obj); void insertAfter(cObject *where, cObject *obj);
Inserts exactly before and after the given object.
cObject *tail();
Returns pointer to the last object in the queue or NULL if the queue is empty.
cObject *pop();
Unlinks and returns the last object in the queue.
cObject *remove(cObject *obj);
Unlinks and returns the object given.
To examine each element in the queue, the cQueueIterator iterator class can be used. Once a cQueueIterator object is created for the queue (the cQueue object), the ++ and -- operators can be used to step from one element of the list to the next/previous one. cQueueIterator is not a descendant of cObject or cIterator.
cQueueIterator(cQueue& q, int a=1);
Constructor, cIterator will walk on the queue passed as argument. The current object will be the first (if a==1) or the last (a==0) object in the queue.
void init(cQueue& q, int a=1);
Reinitializes the iterator object.
cObject *operator()();
Returns the current object.
bool end();
Returns true if we have reached either end of the queue.
cObject& operator++(int); cObject& operator--(int);
Steps to the next/previous object in the queue.
The following inline functions are for finding objects by name.
inline cNetworkType *findNetwork(const char *s);
Equals (cNetworkType *)networks.find(s);
inline cModuleType *findModuleType(const char *s)
Equals (cModuleType *)modtypes.find(s);
inline cLinkType *findLink(const char *s);
Equals (cLinkType *)linktypes.find(s);
inline cFunctionType *findFunction(const char *s);
Equals (cFunctionType *)functions.find(s);
cHead is the head of a cObject chain. cObject and its derived classes contain pointers that enable the objects to be a node in a double-linked list. cObject has member functions to link and unlink to and from double-linked lists (See documentation on cObject::setOwner()) The head of such lists is always a cHead object. The lists are a means that provide that each object in the system is part of an object tree and can be accessed through pointers starting from a given point. The existence of such hierarchy is necessary for a user interface where we want each object to be 'visible' to the user. It is also unavoidable when we want the simulation to be restartable (we need to destroy objects created by the running simulation to start a new one). Last, it enables that all objects can be reached through forEach() on which many algorithms rely (e.g. saveresults()).
For cHead, the dup() and operator=() functions are NOT implemented. dup() would require that every object in the list be duplicated. Since cHead is mostly an internal class and is NOT intended for use by the programmer as a container class, the dup() operation was considered unnecessary.
Construction, destruction, copying:
cHead(const char *name=NULL, cHead *h=&locals, bool init=true );
Constructor. Accepts the object name, the head of the list it should join and a parameter that tells cHead if it should initialize the pointers to the list.
virtual ~cHead();
The destructor deletes all objects in the list that were created on the heap.
Redefined virtual functions
virtual const char *className();
Returns the pointer to the class name string, "cHead".
virtual void forEach(ForeachFunc f);
Calls the function passed for each object in the list.
Member functions:
cObject *find(const char *s);
Searches the list for an object with the given name and returns its pointer. If no such object was found, NULL is returned.
int count();
Returns the number of objects in the list.
To examine each element in the list, the cIterator iterator class can be used. Once a cIterator object is created for the list (for the cHead object), the ++ operator can be used to step from one element of the list to the next one. cIterator is not a descendant of cObject.
cIterator(cHead& h);
Constructor, cIterator will walk on the list passed as argument. The current object will be the first one in the list.
void init(cHead& h);
Reinitializes the iterator object.
cObject *operator()();
Returns a pointer to the current object.
bool end();
Returns true if we reach the end of the list.
cObject& operator++(int);
Advances to the next object in the list.
There are two non-object container classes: cLinkedList and cBag. The first one parallels with cQueue, the second one with cArray.
cLinkedList is a container class that can holds non-object items. cLinkedList acts very similar to cQueue.
Memory management of contained items is controlled by the configPointer() function. As default, pointers are treated as mere pointers, so items are never duplicated or deleted.
Construction, destruction, copying
cLinkedList(cLinkedList& llist);
Copy constructor. Contained items that are owned by the list will be duplicated using the function passed in configPointer() so that the new list will have its own copy of them. By default, there's no duplication function so only the pointers are copied.
cLinkedList(const char *name=NULL);
Constructor. It accepts the object name.
virtual ~cLinkedList();
Destructor calls clear().
virtual cObject *dup(); cLinkedList& operator=(cLinkedList& llist);
Duplication and assignment work all right with cLinkedList. Contained items are treated as configured with configPointer(). By default, only pointers are copied.
Redefined virtual functions
virtual const char *className();
Returns pointer to the class name string,"cLinkedList".
virtual void info(char *buf); virtual const char *inspectorFactoryName();
Redefined.
Member functions
void configPointer(VoidDelFunc delfunc, VoidDupFunc dupfunc, size_t size = 0);
Configures memory management for contained items. Similar to cPar's configPointer() function.
| delete func. | dupl.func. | itemsize | behaviour |
| NULL | NULL | 0 | Pointer is treated as mere pointer - no memory management. Duplication copies the pointer, and deletion does nothing. |
| NULL | NULL | >0 size | Plain memory management. Duplication is done with new char[size]+memcpy(), and deletion is done via delete. |
| NULL or user's delete func. | user's dupfunc. | indifferent | Sophisticated memory management. Duplication is done by calling the user-supplied duplication function, which should do the allocation and the appropriate copying. Deletion is done by calling the user-supplied delete function, or the delete operator if it is not supplied. |
void clear();
As a result, the container will be empty. Contained items will be deleted as configured by configPointer().
int length();
Returns the number of items contained in the list.
bool empty();
Returns true if the list is empty.
void insert(void *p);
Inserts the given object into the list, maintaining the sorting order.
void insertBefore(void *where, void *p); void insertAfter(void *where, void *p);
Inserts exactly before and after the given item.
void *tail();
Returns the last item in the list or null pointer if the list is empty.
void *pop();
Unlinks and returns the last item in the list.
void *remove(void *obj);
Unlinks and returns the given item.
To examine each element in the list, the cLinkedListIterator iterator class can be used. Once a cLinkedListIterator object is created for the list (the cLinkedList object), the ++ and -- operators can be used to step from one element of the list to the next/previous one. cLinkedListIterator is not a descendant of cObject or cIterator.
cLinkedListIterator(cLinkedList& ll, int a=1);
Constructor, cIterator will walk on the list passed as argument. The current item will be the first (if a==1) or the last (a==0) item in the list.
void init(cLinkedList& q, int a=1);
Reinitializes the iterator object.
void *operator()();
Returns the current item.
bool end();
Returns true if we have reached the end of the list.
void *operator++(int); void *operator--(int);
Steps to the next/previous object in the list.
cBag is a container class which is designed to hold non-cObject items without constructors/destructors (ints, doubles, small structs etc.). cBag stores full, bit-by-bit copies of the items inserted (and not the pointers). cBag works as an array, but if it gets full, it grows automatically by a specified delta.
Construction, destruction, copying
cBag(cBag& bag);
Copy constructor.
cBag(const char *name=NULL, int esiz=1,int siz=0,int delt=5, cHead *h=&locals);
Constructor. It takes the object name, the size of an item in bytes, the initial vector size, delta and the pointer to the list it should join as argument.
virtual ~cBag();
Destructor. The items in the object will be deleted.
virtual cObject *dup(); cBag& operator=(cBag& bag);
Duplication and assignment work all right with cBag.
Redefined virtual functions
virtual const char *className();
Returns a pointer to the class name string, "cBag".
virtual void info(char *buf); virtual const char *inspectorFactoryName();
Redefined.
Member functions:
void setup(int esiz,int siz, int delt=5);
Clears the current contents and changes element size, initial array size and delta parameters.
void clear();
Clears the whole contents of the cBag.
int items();
Returns the index of last used position+1.
int add(void *data);
Inserts a new item into the array. A copy will be made of the item pointed to by data. The return value is the item's index in the array.
int addAt(int pos, void *data);
Inserts a new item into the array at the given position.
int find(void *data);
Returns the index of the first item in the array that equals the item pointed to by obj. The comparison is done byte-by-byte. If no such item was found, -1 is returned.
void *get(int m);
Returns a pointer to the mth item in the array or NULL if the mth position is not used.
void *operator[](int m);
The same as get(int m). With the indexing operator, cBag can be used as a vector.
bool isUsed(int m);
Returns true if the mth position exists and an item was inserted there.
bool remove(int m);
Deletes the mth position in the array. Returns true if the mth position was used (i.e. it was actually deleted.)
Header file: carray.h (sim directory)
cWatch is an object shell for an ordinary char, int, long, double, char* or cObject* variable in the module functions. It is provided for the following reason: the cSimpleModule::snapshot() call outputs every object of the simulation into a text file, which is excellent for debugging. Unfortunately, ordinary variables (int, char types etc) do not appear in the snapshot file. cWatch helps this. Use it like this:
int samples; new cWatch( "samples", samples );
Now, the cWatch object will make the samples variable appear in the snapshot file.
The second line can be shortened using the WATCH macro:
WATCH( samples );
Constructors:
cWatch(cWatch& vs); cWatch(const char *name, char& c); cWatch(const char *name, int& i); cWatch(const char *name, long& l); cWatch(const char *name, double& d); cWatch(const char *name, char* &s); cWatch(const char *name, cObject* &o);
Initialize the shell to hold the given variable.
virtual const char *className(); virtual cObject *dup(); ...
All usual virtual functions redefined.
virtual void info(char *buf); virtual void writeContents(ostream& os);
These functions are redefined to display the value of the variable. Output is like this: "int samples = 12 (12U, 0xC)"
virtual void printTo(char *buf);
Does actual work for info() and writeContents().
Header file: coutvect.h (sim directory)
There are two classes: cOutVector and cOutFileMgr. cOutFileMgr and cOutVector work together to allow the user save several "series" or "output vectors" (a series of numbers or number pairs which are produced during simulation as results) to a common file called "output vector file" or simply "output file".
Users don't need to use cOutFileMgr directly. There is only one instance of cOutFileMgr (in cSimulation), it stores the common file pointer and gives out unique identifiers to cOutVector objects.
There can be several cOutVectors, each one handles one output vector. Data are written into a common file.
The output file is ASCII and can be read by a spreadsheet etc. It should cause no problem to sort out values for one particular output vector.
cOutVector is responsible for writing simulation data (an output vector) to a file. A cOutVector object can write doubles or pair of doubles to the "statistical output file". The file consists of label lines and data lines.
Here are some sample lines:
vector 5 "subnet[4].term[12]" "response time" 1 5 12.895 2355.66666666 5 14.126 4577.66664666 vector 6 "subnet[4].srvr" "queuelen+queuingtime" 2 6 16.960 2.00000000000 .63663666 5 23.086 2355.66666666 6 24.026 8.00000000000 .44766536
There are label lines (beginning with "vector") and data lines.
A label line introduces a new vector. The columns: "vector", vector ID, module of creation, name of cOutVector object, number of data (single numbers or pairs will be written).
Columns of a data line: vector ID, actual simulation time, one or two double values.
One can use UNIX tools like sed, awk or perl to extract a particular vector etc. from the file, or/and read it in spreadsheets like Excel.
The cOutVector::record() member is used to output a value (or a value pair). This will generate a data line in the file, unless the output vector is disabled or the current simulation time is outside a specified interval (see member functions).
Construction, destruction, copying:
cOutVector(cOutVector& r);
Copy constructor.
cOutVector (const char *s=NULL, int tupl=1);
Constructor. Accepts the object name and the multiplicity of the data it should write to the file at a time. Possible values of tuple are 1 or 2.
virtual ~cOutVector();
Destructor.
Redefined virtual functions
virtual const char *className();
Returns a pointer to the class name "cOutVector".
virtual cObject *dup();
Dupping does not make much sense, not implemented.
virtual void info(char *buf);
Redefined.
Member functions
void record (double value);
Records one data to the file. It can be used only in the case if the instance of cOutVector was created with tupl=1, otherwise it gives an error message.
void record (double value1, double value2);
Records two values to the file. It can be used only in the case if the instance of cOutVector was created with tupl=2, otherwise it gives an error message.
void enableWrite();
Makes writing to the file enabled. It is enabled by default.
void disableWrite();
Makes writing to the file disabled. It is enabled by default.
void setStartTime(simtime_t t);
Sets StartTime to t. Data is recorded to the file only if simulation time > StartTime. The default value of StartTime is 0.
void setStopTime(simtime_t t);
Sets StopTime to t. Data is recorded to the file only if the simulation time < StopTime. The default value of StopTime is 0 which means no Stoptime.
This class is responsible for handling the output file for cOutVectors. Users don't need to use cOutFileMgr directly, only through cOutVector.
Constructor, destructor, copying
cOutFileMgr(const char *s=NULL);
Constructor.
virtual ~cOutFileMgr();
Destructor. Closes the output file if it is still open.
Redefined virtual functions
virtual const char *className();
Returns a pointer to the class name "cOutFileMgr".
Member functions
void setFileName(const char *s);
Sets the name of the statistical output file. This name will be set at the time of the next call of openFile().
void openFile();
Opens the statistical output file with the currently set fileName.
void closeFile();
Closes the statistical output file
long getNewID();
Returns a unique ID for constructing a new cOutVector object. The user should not explicitly call this function.
FILE *getHandle();
Returns the file pointer of the statistical output file.
Header file: cstat.h (sim directory)
The statistical data collection classes: cStatistic, cStdDev, cWeightedStdDev, cDensityEstBase, cHistogramBase, cVarHistogram, cEqDHistogramBase, cLongHistogram, cDoubleHistogram, cPSquare. and cKSplit.
Several of those are abstract classes. The ones which are not are: cStdDev, cVarHistogram, cLongHistogram, cDoubleHistogram, cPSquare. and cKSplit.
cStatistic is the base class for all statistical data collection classes. cStatistic itself adds no data members or algorithms to cObject, it only defines virtual functions that will be redefined in descendants. No instance of cStatistic can be created.
Construction, destruction, copying
cStatistic(cStatistic& r);
Copy constructor.
cStatistic(const char *s=NULL);
Constructor, creates an object with the given name
virtual ~cStatistic();
The destructor does nothing.
cStatistic& operator=(cStatistic& res)
The assignment operator is present since descendants may refer to it.
Redefined virtual functions
virtual const char *className();
Returns a pointer to the class name string, "cStatistic".
New non-virtual member functions
void setGenK(int gen_nr);
Sets the index of the random number generator to use when the object has to generate a random number based on the statistics stored.
void addTransientDetection(cTransientDetection *object); void addAccuracyDetection(cAccuracyDetection *object);
Assigns transient and accuracy detection objects to the statistic object.
cTransientDetection *transientDetectionObject(); cAccuracyDetection *accuracyDetectionObject();
Returns the assigned transient and accuracy detection objects.
New virtual member functions
These pure virtual functions provide a common interface to all cStatistic classes.
void collect(double val);
Collect one value.
virtual long samples(); virtual double min(); virtual double max(); virtual double mean(); virtual double variance(); virtual double stddev(); virtual double sum(); virtual double sqrSum();
In derived classes, these functions return the number of values collected, the smallest/largest value, the mean, the standard deviation, the sum and the squared sum of the collected data, respectively.
virtual double random();
The function generates a random number based on the collected data.
virtual void clearResult();
This function should be redefined in derived classes to clear the results collected so far.
virtual void saveToFile(FILE *);
Writes the contents of the object into a text file.
virtual void loadFromFile(FILE *);
Reads the object data from a file written out by saveToFile()(or written "by hand")
cStdDev is designed to collect doubles and calculate data such as the minimum/maximum value, the mean and the standard deviation for them.
Construction, destruction, copying
cStdDev(cStdDev& r); cStdDev(const char *s=NULL); virtual ~cStdDev(); virtual cObject *dup(); cStdDev& operator=(cStdDev& res);
Constructors, destructor, duplication and assignment.
Redefined virtual functions
virtual const char *className();
Returns a pointer to the class name string, "cStdDev".
virtual void info(char *buf); virtual const char *inspectorFactoryName(); virtual void writeContents(ostream& os);
Redefined cObject functions.
void collect(double val); virtual void clearResult(); virtual long samples(); virtual double min(); virtual double max(); virtual double mean(); virtual double stddev(); virtual double variance(); virtual void saveToFile(FILE *); virtual void loadFromFile(FILE *);
Redefined cStatistic functions.
virtual double random();
Redefined cStatistic function. cStdDev's random number generator returns numbers of normal distribution with the current mean and standard deviation.
cWeightedStdDev is designed to collect doubles and calculate weighted statistics of them.
Construction, destruction, copying
cWeightedStdDev(cWeightedStdDev& r); cWeightedStdDev(const char *s=NULL); virtual ~cWeightedStdDev(); virtual cObject *dup(); cWeightedStdDev& operator=(cWeightedStdDev& res);
Constructors, destructor, duplication and assignment.
Redefined virtual functions
virtual const char *className();
Returns a pointer to the class name string, "cWeightedStdDev".
virtual void info(char *buf); virtual const char *inspectorFactoryName(); virtual void writeContents(ostream& os);
Redefined cObject functions.
void collect2(double val, double weight);
New member function.
virtual void clearResult(); virtual long weights(); virtual double min(); virtual double max(); virtual double mean(); virtual double stddev(); virtual double variance(); virtual void saveToFile(FILE *); virtual void loadFromFile(FILE *);
Redefined cStdDev functions.
virtual double random();
cWeightedStdDev's random number generator returns numbers of normal distribution with the current mean and standard deviation.
Common base class for density estimation classes. Provides several pure virtual functions, so it is an abstract class, no instances can be created.
For the histogram classes, you need to specify the number of cells and the range. Range can either be set explicitly or you can choose automatic range determination.
Automatic range estimation works in the following way:
You may also explicitly specify the lower or upper limit and have the other end of the range estimated automatically. The setRange...() member functions of cDensityEstBase deal with setting up the histogram range. It also provides pure virtual functions transform() etc.
Subsequent observations are placed in the histogram structure. If an observation falls out of the histogram range, the underflow or the overflow cell is incremented.
Construction, destruction, copying
cDensityEstBase(cDensityEstBase& r); cDensityEstBase (const char *s=NULL); virtual ~cDensityEstBase(); cDensityEstBase& operator=(cDensityEstBase& res);
Constructors, destructor, assignment.
Redefined virtual functions
virtual const char *className() virtual void writeContents(ostream& os); virtual void collect(double val); virtual void clearResult(); virtual void saveToFile(FILE *); virtual void loadFromFile(FILE *);
New virtual functions
virtual bool transformed(); virtual void transform();
First one returns whether the object is transformed or not; second one forces a transformation.
virtual unsigned cells(); virtual double basepoint(unsigned k); virtual unsigned cell(unsigned k); virtual double cellPDF(unsigned k);
Obtaining histogram data out of the object.
virtual void setRange(double lower, double upper); virtual void setRangeAuto(int num_firstvals, double range_ext_fact); virtual void setRangeAutoLower(double upper, int num_firstvals, double range_ext_fact); virtual void setRangeAutoUpper(double lower, int num_firstvals, double range_ext_fact); virtual void setNumFirstVals(int num_firstvals);
Range setting.
virtual double pdf(double x) = 0; virtual double cdf(double x) = 0;
Density function and cumulated density function at a given x.
virtual unsigned long underflowCell() virtual unsigned long overflowCell()
Returns number of observations that fall out of the histogram range.
Base class for histogram classes.
Variable bin size histogram You may add cell (bin) boundaries manually, or .let the object create cells with equal number of observations in them (or as close to that as possible).
Constructor
cVarHistogram(const char *s=NULL, int numcells=11, int transformtype=HIST_TR_AUTO_EPC_DBL);
The third argument can be one of HIST_TR_NO_TRANSFORM, HIST_TR_AUTO_EPC_DBL, HIST_TR_AUTO_EPC_INT.
New member function
virtual void addBinBound(double x);
If HIST_TR_NO_TRANSFORM was passed in the constructor call, you may specify cell (bin) bounds manually before collection starts.
Base class for equal cell size histograms.
cLongHistogram is derived from cEqdHistogramBase which contains most of the functionality. The histogram is set up in the following way:
Construction, destruction, copying
cLongHistogram(cLongHistogram& r);
Copy constructor.
cLongHistogram(const char *s=NULL, unsigned num_cells=10)
Constructor that takes the object name and the number of cells.
virtual ~cLongHistogram();
Destructor.
virtual cObject *dup(); cLongHistogram& operator=(cLongHistogram& res);
Duplication and assignment work all right with cLongHistogram.
Redefined virtual functions
virtual const char *className();
Returns a pointer to the class name string, "cLongHistogram".
virtual void info(char *buf); virtual const char *inspectorFactoryName();
Redefined.
virtual void transform();
If the result collection is in the first phase, transforms the values into histogram. If already in the second phase, the function does nothing.
virtual unsigned cells();
Returns the number of cells.
virtual double basepoint(unsigned k);
Returns the kth basepoint.
virtual unsigned cell(unsigned k);
Returns the number of samples that fell into the kth subinterval.
virtual double cellPDF(unsigned k);
Returns the calculated PDF in the kth subinterval.
virtual double random();
Returns a random number based on the distribution collected. If no values have been collected, it returns 0; when in initial collection phase, it returns one of the stored observations; after the histogram has been set up, a random integer is returned.
virtual void clearResult ();
Clears all results collected so far.
cDoubleHistogram is derived from cEqdHistogramBase; see that for more information.
Construction, destruction, copying
cDoubleHistogram(cDoubleHistogram& r);
Copy constructor
cDoubleHistogram (const char *s=NULL, unsigned int num_cells=10);
Constructor that takes the object name, the number of subintervals and the range factor as argument.
virtual ~cDoubleHistogram();
Destructor.
virtual cObject *dup(); cDoubleHistogram& operator=(cDoubleHistogram& res);
Duplication and assignment work all right with cDoubleHistogram.
Redefined virtual functions
virtual const char *className();
Returns a pointer to the class name string, "cDoubleHistogram".
virtual void info(char *buf); virtual const char *inspectorFactoryName();
Redefined.
virtual void writeContents(ostream& os);
Writes the object contents to the stream.
virtual void transform();
If the result collection is in the first phase, transforms the values into histogram. If already in the second phase, the function does nothing.
virtual unsigned cells();
Returns the number of cells.
virtual double basepoint(unsigned k);
Returns the kth basepoint.
virtual unsigned cell(unsigned k);
Returns the number of samples that fell into the kth subinterval.
virtual double cellPDF(unsigned k);
Returns the calculated PDF in the kth subinterval.
virtual double random();
Returns a random number based on the distribution collected. If no values have been collected, it returns 0; when in initial collection phase, it returns one of the stored observations; after the histogram has been set up, a random integer is returned.
virtual void clearResult();
Clears the distribution collected so far.
Header file: cpsquare.h, cksplit.h (sim directory)
Implements the P2 algorithm.
Implements the k-split algorithm.
Header file: cdetect.h (sim directory)
Detection of the end of the transient period and a certain result accuracy is supported by OMNeT++. The user can attach transient detection and result accuracy objects to a result object (cStatistic's descendants).
Virtual base class for transient detection.
An algorithm for transient detection. Uses sliding window approach with two windows, and checks the difference of the two averages to see if the transient period is over.
Virtual base class for result accuracy detection.
An algorithm for result accuracy detection. The actual algorithm: divide the standard deviation by the square of the number of values and check if this is small enough.
cFSM is not yet documented here. See the User Manual.
cTopology is not yet documented here. See the User Manual.
Header file: cenvir.h (envir directory)
cEnvir is the user interface class. cEnvir is not a descendant of cObject and there is only one instance of cEnvir, ev. ev is a static object. All I/O is done via the member functions of ev.
Construction, destruction
cEnvir(); ~cEnvir();
The constructor and the destructor in most cases do nothing.
User interface functions
void inspect(cObject *object);
Creates an inspector window for the object.
void objectDeleted(cObject *object);
Notifies the environment that the object no longer exists. The user interface should close all inspector windows for the object and remove it from object lists currently displayed. cObject's destructor automatically calls this function.
void messageSent(cMessage& msg);
Notifies the environment that a message has been sent. The user interface then can display the message in a text window, animate the message's travel in the network's drawing. activate conditional breakpoints and so on. This function is automatically called right after cSimpleModule's send() placed the message into the future events' queue.
Module function I/O
void printfmsg(const char *fmt,...);
Displays a message in dialog box. This function should not be used too much by simple modules, if ever.
void printf(const char *fmt="\n",...);
Simple modules can output text into their own window through this function. The text is expected in printf() format (format string + arguments).
void puts(const char *s);
Similar to cEnvir::printf(), but just writes out its argument string with no formatting.
bool askf(char *buf, int len, const char *profmt,...);
Pops up a dialog, displays the message given in 'profmt' and following arguments in printf() format and reads a line (maximum len characters) from the user into the buffer 'buf'. Returns true if the user pressed the Cancel button.
bool gets(const char *prompt, char *buf, int len=255);
Similar to cEnvir::askf(), but just writes out the prompt message string with no formatting.
bool askyesno(const char *msgfmt,...);
Puts a yes/no question to the user. The question itself is expected in the printf() format (format string + arguments). The true return value means yes, false means no.
cEnvir& prompt(const char *s);
Sets the prompt for subsequent >> operators. There is only one global prompt text that is shared between all simple modules, so between setting the prompt string and asking the user, no receive(), peekmsg(), messages() or wait() call is recommended. Or, the user may experience funny things. . .
const char *prompt();
Returns the prompt string.
Overloaded operators
Overloaded operators provide iostream-like I/O for cEnvir.
inline cEnvir& operator<< (cEnvir& ev, const signed char *s); inline cEnvir& operator<< (cEnvir& ev, const unsigned char *s); inline cEnvir& operator<< (cEnvir& ev, unsigned char c); inline cEnvir& operator<< (cEnvir& ev, signed char c); inline cEnvir& operator<< (cEnvir& ev, short i); inline cEnvir& operator<< (cEnvir& ev, unsigned short i); inline cEnvir& operator<< (cEnvir& ev, int i); inline cEnvir& operator<< (cEnvir& ev, unsigned int i); inline cEnvir& operator<< (cEnvir& ev, long l); inline cEnvir& operator<< (cEnvir& ev, unsigned long l); inline cEnvir& operator<< (cEnvir& ev, float f); inline cEnvir& operator<< (cEnvir& ev, double d); inline cEnvir& operator<< (cEnvir& ev, long double d);
Output operators for all fundamental data types.
inline cEnvir& operator* (cEnvir& ev, const signed char *s); inline cEnvir& operator* (cEnvir& ev, const unsigned char *s);
The * operator can be used to replace ev.prompt(). For example:
ev * "How many?" >> n; inline cEnvir& operator>> (cEnvir& ev, signed char *s); inline cEnvir& operator>> (cEnvir& ev, unsigned char *s); inline cEnvir& operator>> (cEnvir& ev, char& c); inline cEnvir& operator>> (cEnvir& ev, unsigned char& c); inline cEnvir& operator>> (cEnvir& ev, short& i); inline cEnvir& operator>> (cEnvir& ev, int& i); inline cEnvir& operator>> (cEnvir& ev, long& l); inline cEnvir& operator>> (cEnvir& ev, unsigned short& i); inline cEnvir& operator>> (cEnvir& ev, unsigned int& i); inline cEnvir& operator>> (cEnvir& ev, unsigned long& l); inline cEnvir& operator>> (cEnvir& ev, float& f); inline cEnvir& operator>> (cEnvir& ev, double& d); inline cEnvir& operator>> (cEnvir& ev, long double& d);
Input operators for all fundamental data types. But beware, each >> operator reads a whole line!
cSimulation
A class that holds the module tree of the network being simulated. It has only one instance, simulation.
cModuleType
Class for simple module types.
cLinkType
Class for channel types.
cFunctionType
Class for function types. Used by the code generated by NEDC.
cNetworkType
Class for pre-compiled networks.
cClassRegister
Class for registering a class.
Simulation-related
[JAIN91] Jain, Raj: The Art of Computer Systems Performance Analysis. Wiley, New York, 1991.
[BFS86] Bratley P., Fox, B. L. and Schrage, L. E.: A Guide to Simulation. Springer-Verlag, New York, 1986.
[JCH85] Jain, Raj and Chlamtac, Imrich: The P2 Algorithm for Dynamic Calculation of Quantiles and Histograms without Storing Observations, Communications of the ACM, 28(10), 1076-1085, 1985.
[PON91] Pongor, György: OMNET: An Object-Oriented Network Simulator. 1991 ??
[PON92] Pongor, György: Statistical Synchronization: A Different Approach of Parallel Discrete Event Simulation. Lappeenranta University of Technology, Data Communications Laboratory, Lappeenranta, Finland, 1992
[PON93] Pongor, György: On the Efficiency of the Statistical Synchronization Method. European Simulation Symposium (ESS'93), Delft, The Netherlands, Oct. 25-28, 1993
[KOF95] Kofoed, Stig: Portable Multitasking in C++. Dr. Dobb's Journal, November 1995. ftp://ftp.mv.com/pub/ddj/1995/1995.11/mtask.zip
TBD include papers of Gabor Lencse
OMNeT++-related research papers
[VAR99] "Using the OMNeT++ Discrete Event Simulation System in Education". András Varga. IEEE Transactions on Education, November 1999 CD-ROM issue; abstract in vol. 42, no. 4, pp. 372, November 1999.
[VAR98a] "K-split - On-Line Density Estimation for Simulation Result Collection". András Varga. In the Proceedings of the European Simulation Symposium (ESS'98). October 26-28, 1998. Nottingham, UK.
[VAR98b] "Parameterized Topologies for Simulation Programs". András Varga. In the Proceedings of the Western Multiconference on Simulation (WMC'98) / Communication Networks and Distributed Systems (CNDS'98). January 11-14, 1998. San Diego, CA.
[V&F97] "The K-Split Algorithm for the PDF Approximation of Multi-Dimensional Empirical Distributions without Storing Observations". András Varga and Babak Fakhamzadeh. In Proceedings of the 9th European Simulation Symposium (ESS'97), pp.94-98. October 19-22 1997, Passau, Germany.
[V&P97] "Flexible Topology Description Language for Simulation Programs". András Varga and György Pongor. In Proceedings of the 9th European Simulation Symposium (ESS'97), pp.225-229. October 19-22 1997, Passau, Germany.
Former OMNeT++ documents
[OMN1] Vass Zoltán.: PVM Extension of OMNeT++ to Support Statistical Synchronization. Diploma Thesis, Technical University of Budapest, 1996 (in Hungarian).
[OMN2] André Maurits, George van Montfort and Gerard van de Weerd: OMNeT++ extensions and examples. Technical University of Budapest, Dept. of Telecommunications, 1995.
[OMN3] Jan Heijmans, Alex Paalvast, Robert van der Leij: Network simulation using the JAR compiler for the OMNeT++ simulation system. Technical University of Budapest, Dept. of Telecommunications, 1995.
[OMN4] Varga András.: OMNeT++ - Portable User Interface for the OMNeT++ Simulation System. Diploma Thesis, Technical University of Budapest, 1994 (in Hungarian).
[OMN5] Lencse Gábor: Graphical Network Editor for OMNeT++. Diploma Thesis, Technical University of Budapest, 1994 (in Hungarian).
[OMN6] Varga András.: OMNeT++ - Portable Simulation Environment in C++. TDK work, Technical University of Budapest, 1992 (in Hungarian).
Other simulation software
See web siteTBD
C++ language
Too many books to list.
Cyg-Win32
[CYGWIN] http://sourceware.cygnus.com/cygwin/top.html
DJGPP
[DJGPP1] Official DJGPP Home Page: http://www.delorie.com/djgpp
PVM
[PVM1] The Official PVM Home Page. http://www.epm.ornl.gov/pvm/pvm_home.html
[PVM2] http://www.sp2.uni-c.dk/PVM/PvmIntro.html
[PVM3] http://www.cse.ogi.edu/DISC/projects/mist/related-work/pvm.html
Turbo Vision
[TV1] Borland C++ 3.1 Manuals. Borland International, 1992.
[TV2] The TVPlus Archieve. http://wvnvm.wvnet.edu/~u6ed4/tvhome.htm
[TV3] Sierwald, Joern: 32-bit Portable Turbo Vision. http://wvnvm.wvnet.edu/~u6ed4/tvptsier.htm
TCL/TK
[TCLTK1] Welch, Brent: Practical Programming in Tcl and Tk. Prentice-Hall, 1995
[TCLTK2] HyperTcl. http://web.cs.ualberta.ca/~wade/HyperTcl/
[TCLTK3] TCL WWW Info. http://www.sco.com/Technology/tcl/Tcl.html
Gnuplot
[GPLOT1] Brief tutorial:
http://nacphy.physics.orst.edu/DATAVIS/datavis.html
[GPLOT2] Reference:
http://www.cm.cf.ac.uk/Latex/Gnuplot/gnuplot.html
[PMTV1] PlotMTV:
http://cauchy.math.edu/workshop/Plotmtv/plotmtv.html
Xmgr
[XMGR1] Brief tutorial:
http://nacphy.physics.orst.edu/DATAVIS/xmgr.html