University of Bremen – Communication Networks – www.comnets.uni-bremen.de

Comparison of Different Network Simulation Tools

2 University of Bremen – Communication Networks – www.comnets.uni-bremen.de

Overview

Network Simulator Overview

Comparison of Different Network Simulators Random Number Generators

Statistical Evaluation

Modules & Libraries

……

Other Network Simulation Tools

Network Simulator

A network simulator is a piece of software that simulates a network, without an actual network being present.

Purposes: To test scenarios that might be particularly difficult or

expensive to implement on real hardware/system

To test new networking protocols or changes to existing protocols in a controlled and reproducible environment

To predict the behavior of networks and applications under different scenarios (situations)

Network Simulation Software

Open source CNCL

openWNS

NS-2, NS-3

OMNET++

……

Commercial OPNET (but free for

academic use)

Telelogic Tau (SDL)

QualNet

……

Other discrete-event simulation software http://www.topology.org/soft/sim.html

OPNET

Provides a comprehensive development environment supporting the modeling of communication networks (include protocols, technologies) and distributed systems WLAN, UMTS, LTE, WiMAX, IPv6,

MANET, MPLS, Sensor Networks, etc. Perform discrete event simulations

Application performance management Network operations Capacity planning and design Network R&D

Environment incorporates tools for all phases of a study, including model design, simulation & data collection, and analysis

Hierarchical Models networks and subnetworks nodes, e.g., packet generators, queues,

processors process model: state diagram and C code

http://www.opnet.com/

CNCL: Communication Networks Class Library

Class Library in C++ with the following characteristics: tree like class structure

run time type information and test

weak typing and general container classes

general class library with basic data types

Portability

Main components for Simulation Random Numbers:

efficient & flexible generation

Statistical Evaluation:

Moments, Batch Means, LRE

(Pure) Event oriented scheduling

openWNS

Wireless Network Simulator (WNS) Wireless:

Model the properties of radio communication Model mobile nodes

Network: Build a network of communicating nodes Support multiple different node types Support different network protocols

Simulator: Build models to evaluate real systems (present and future) Discrete event-driven Stochastic

Example Scenarios:

Cellular (e.g. 3GPP LTE) IEEE 802.11g, s “WLAN” IEEE 802.16e, m “WiMAX” Personal Area Network (PAN)

SDL - Specification and Description Language

Formal specification languages

Performance evaluation of formally specified protocols by means of simulation and emulation

Prediction of hardware requirements, e.g. used amount of memory and whether real-time conditions are met

Emulation of SDL implementations for target processor systems

In Comnets, only simulation is considered

SDL: Hierarchy and Communication

Hierarchy: Environment (Umgebung)

System contains one or more blocks

Block can consist of blocks (recursive)

Block contains one or more processes

Process

Communication System communicates with the environment via channels (Kanäle)

Blocks communicate via channels

Processes communicate via signal routes (Signalwege)

Processes can create other processes and can terminate themselves

Communication is realized by messages that are called signals. Signals are generated and consumed by processes and forwarded by blocks.

Building a simulation using SDL

Tool to develop SDL programs: Telelogic Tau Graphical editor with online syntax check

for system, block and process level

syntactic and semantic analysis

SDL to C translation

Optionally: Editing C files (text) for Abstract Data Types (ADTs)

Compilation of all C files into object files

Compilation of special C files shipped with the Telelogic Tau tool which contain the code for process control, signal control and scheduling

Linking the files to executable simulator

SDL specification

real scenario

C implementation

simulation run

statistical evaluation

compilation

Comparison of Different Network Simulators - Random Number Generators CNCL

6 Base generators (CNRNG): ACG, Fibo, File, LCG, MLCG, Taus 20 Distributions (CNRandom): e.g. NegExp, Normal, HyperExp, Binomial,

Uniform, Erlang, poission, rayleigh

OPNET Default: based on the RNG of the operation system, has a period of 231-1

Sun Solaris: random ( ) Microsoft Windows: random ( ) Random numbers are drawn from one random number sequence

initialized with the value of the “seed” environment attribute In the latest version of OPNET, there is a newer random number generator:

"Mersenne-Twister“ which generates higher quality random numbers with a period of 2607-1 Reference: http://en.wikipedia.org/wiki/Mersenne_twister

Other alternative method: use the RNG from CNCL Reference: M. Becker, T. L. Weerawardane, X. Li and C. Görg, "Extending OPNET

Modeler with External Pseudo Random Number Generators and Statistical Evaluation by the Limited Relative Error Algorithm" in COST Action 285: Modeling and Simulation Tools for Research in Emerging Multi-Service Telecommunications

22 Distributions available: e.g. NegExp, Normal, HyperExp, Binomial, Uniform, Erlang, poission, rayleigh, Pareto, Laplace

Comparison of Different Network Simulators - Random Number Generators

openWNS Uses boost C++ library “Random” Default uniform RNG is Mersenne Twister MT19937

Negligible correlation Period: 219937 – 1 Fast

Many distributions available

SDL (Telelogic Tau)

Not include any RNG, needs to use external RNGs (e.g. from CNCL)

Matlab R12 lagged Fibonacci

NS-2 MRG32k3a

Comparison of Different Network Simulators Programming Language

Statistical Evaluation

Results Modules

CNCL C++ Moments Batch Means (BM) LRE (cdf, ccdf, rel. error, correlation factor)

-> Log files Base classes: • CNCL • CNObject • CNClass • CNParam Do not define a specific network model

OPNET/ OMNET++

C, C++ Moments cdf, pdf, etc (LRE and BM needs to use CNCL)

GUI or Log files

Traffic Model, TCP, UDP IPv4, IPv6, UMTS, Ethernet, WLAN, WiMAX, ATM, etc.

openWNS Source code: C++ Configuration: Python

Moments cdf, pdf, LRE (BM needs to use CNCL)

GUI or Log files

Traffic Model, TCP, UDP IPv4, Ethernet, WLAN, WiMAX, LTE, WiMedia, OFDMA, channel model, etc

SDL SDL Telelogic Tau - SDL to C translation

needs to use CNCL or other tools for statistical evaluation

-> Log files

Other Network Simulation Tools

OMNET++ an extensible, modular, component-based C++ simulation library

and framework

OMNeT++ is free for academic and non-profit use, and it is a widely used platform in the global scientific community

a discrete event simulation environment with GUI support

for the simulation of communication networks, complex IT systems, queueing networks or hardware architectures

simulation models: TCP/IP networks, peer-to-peer networks, LAN/MAN protocols, Ad-hoc, wireless and sensor networks.

the software can be obtained at:

– http://www.omnetpp.org

NS-2 Modified from REAL Network Simulator A discrete event simulator

Packets, links, queues, protocols Simulation of data packet flow Link layer features Wired and wireless features

Simulation platform Freely distributed, open source Developed by researchers Extended to consider current developments

Modeling Network protocols Collection of Various protocols at multiple layers

TCP(reno, tahoe, vegas, sack) MAC(802.11, 802.3, TDMA) Ad-hoc Routing (DSDV, DSR, AODV) Mobiwan (MIPv6) Sensor Network (diffusion, gaf), Bluetooth. UMTS, GPRS, Multicast protocols, Satellite protocols, and many others

NS-2 (cont’) Operating System (OS)

Linux (Mandrake, Debian, Suse) FreeBSD SunOS, Solaris Windows9x/2000/XP Versions later than 2.1b9 of ns work under Windows using Cygwin

C++ compiler (gcc or Visual C++) TCL / TK Follow-up simulation tool: NS-3

Download NS-2

Website: http://www.isi.edu/nsnam/ns/ns-build. html

Install ns-2 on Debian:

Website: http://wiki.splitbrain.org/ns2

Trouble shooting, Bug Fixes, and Help

Website: http://www.isi.edu/nsnam/ns/ns-problems.html

QualNet

QualNet A network evaluation software that predicts wireless, wired and

mixed-platform network and networking device performance. For the testing, optimization and integration of several network

technologies in a lab environment. Provide advanced Propagation (ALE/ASAPS, TIREM, Urban

Propagation), Advanced Wireless, Cellular, Military Radios, Multimedia and Enterprise, Network Emulation Interface, Satellite, and Standard Interfaces.

Designed to take full advantage of the multi-threading capabilities of multi-core 64-bit processors, QualNet supports simulation of thousands of network nodes.

The software can be obtained at: - http://www.qualnet.com/products/download.php.

UNDERSTANDING DEPENDENT RANDOM NUMBERS

Overview

The effect of using dependent RNG’s

Modeling MM1 queuing system using OPNET

Simulation results

Dependent RNG’s

It is often a mistake to use the same type of base RNG for different distributions in a simulation.

If the seed number of the used RNG’s are the same or even different but not far away from each other then a correlation between them will exist.

This leads to wrong results.

It will be clear later in this presentation what is the effect of dependent RNG’s and how it is effecting the results

MM1 queuing system

MM1 queuing system will be used since it’s the simplest system where the theoretical results are already known.

The MM1 system will have the following parameters: λ = 4

μ = 5

ρ = 4 / 5 = 0.8

The theoretical results are: Mean queue length LQ = ρ2 / (1 – ρ) = 3.2

Mean queue waiting time = WQ = LQ / λ = 0.8

MM1 model in OPNET

Simulations

Both the arrival rate and the service rate are negative exponentially distributed The MM1 system needs two RNG’s to be simulated

Two simulations are performed where:

The first simulation is performed with OPNET RNG (which is

mainly the independent case)

The second simulation is preformed with Multiplicative Linear Congruential RNG, where both distributions use it with the same seed number (This is the dependent case)

The mean queue length and the mean queue waiting time are

evaluated

Simulation results

Mean Queue waiting time Mean Queue length

Conclusion

When performing a simulation one should be careful in using RNG’s

In order to have correct results, the used RNG’s:

If different object for the RNG’s of the same type are used

then the seed number of them should be as far as possible in order not to get correlation between them.

Or using the same base RNG, for all the distributions.

Or using different object for the RNG’s but with different types.