Comparison of Different Network Simulation Tools. University of Bremen Communication Networks

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1 Comparison of Different Network Simulation Tools University of Bremen Communication Networks

2 Overview Network Simulator Overview Comparison of Different Network Simulators Random Number Generators Statistical Evaluation Modules & Libraries Other Network Simulation Tools 2 University of Bremen Communication Networks

3 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)

4 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

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

5 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

6 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

7 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 g, s WLAN IEEE e, m WiMAX Personal Area Network (PAN)

8 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

9 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.

10 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 real scenario SDL specification C implementation compilation simulation run statistical evaluation

11 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 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 Reference: 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

12 Comparison of Different Network Simulators - Random Number Generators openwns Uses boost C++ library Random Default uniform RNG is Mersenne Twister MT19937 Negligible correlation Period: 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

13 Comparison of Different Network Simulators Programming Language Statistical Evaluation Results Modules CNCL C++ Moments Batch Means (BM) LRE (cdf, ccdf, rel. error, correlation factor) OPNET/ OMNET++ openwns SDL C, C++ Moments cdf, pdf, etc (LRE and BM needs to use CNCL) Source code: C++ Configuration: Python SDL Telelogic Tau - SDL to C translation Moments cdf, pdf, LRE (BM needs to use CNCL) needs to use CNCL or other tools for statistical evaluation -> Log files Base classes: CNCL CNObject CNClass CNParam Do not define a specific network model GUI or Log files GUI or Log files -> Log files Traffic Model, TCP, UDP IPv4, IPv6, UMTS, Ethernet, WLAN, WiMAX, ATM, etc. Traffic Model, TCP, UDP IPv4, Ethernet, WLAN, WiMAX, LTE, WiMedia, OFDMA, channel model, etc

14 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:

15 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

16 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: html Install ns-2 on Debian: Website: Trouble shooting, Bug Fixes, and Help Website:

17 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: -

18 UNDERSTANDING DEPENDENT RANDOM NUMBERS

19 Overview The effect of using dependent RNG s Modeling MM1 queuing system using OPNET Simulation results

20 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

21 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 = ρ 2 / (1 ρ) = 3.2 Mean queue waiting time = WQ = LQ / λ = 0.8

22 MM1 model in OPNET

23 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

24 Simulation results Mean Queue waiting time Mean Queue length

25 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.

Introduction to OMNeT++

Introduction to OMNeT++ Acknowledgment The source material for this presentation was borrowed from the OMNeT++ User Manual Version 4.1 What is OMNeT++ OMNeT++ is an object-oriented modular discrete event