Chapter 4

1. Chapter 4 TCP/IP Network Simulation

2. Objectives • Appreciate the role of simulation in performance evaluation of TCP/IP networks • Acquire the knowledge needed to conduct steady state simulation • Master basic skills for analyzing confidence level • Describe types of simulation tools available • Familiarize with the capabilities of popular simulation tools

3. Contents • Why use simulation • Systematic simulation study • Types of simulations • Simulation validation and verification • Confidence level of simulation results • Simulation with self similar traffic • Simulation tools

4. Why Use Simulation

5. Why Use Simulation • Predict performance for proposed network • Allow performance evaluation under a wide variety of network conditions • Compare alternative architectures under identical and repeatable conditions • Produce results closer to reality • Validate analytical results

6. Systematic Simulation Study

7. Systematic Simulation Study • Pre-software stage • Define problem/objective: • consult all relevant people • Design network model and select fixed parameters • Topology, network parameters (bandwidth, delays, traffic model, etc.) • Select performance metrics: • Throughput, packet delay, jitter, etc. • Select variable parameters: • The one that could have an important impact in the performance: i.e. BER in Wireless

8. Systematic Simulation Study (Cont.) • Software stage • Model construction: • Reference model (step 2) into software. • Simulation configuration: • To produce relevant performance data. • Simulation execution/Data collection • Result presentation and interpretation

9. Types of Simulation

10. Types of simulations • Continuos vs. discrete event: • Continuo: Calor transferido en una barra de metal. • Discreto: Tamaño de la fila en un supermercado. • Terminating vs. steady state: • Terminating: simulating the peak hour, or simulating the downloading of a number of documents. • Steady state: Long-term packet lost. • Synthetic vs. trace-driven: • Synthetic data: using random traffic generators, this are easy to oppearte.

11. Steady State • If we are interested in asymptotic behavior of a network system, we cannot use terminating simulations • Must continue until it reaches steady state

12. Trace-Driven Simulations • Actual network traces can be used as simulation input • Results can be more convincing

13. Validation and Verification

14. Simulation Validation and Verification • Validation: • Make sure that the assumptions are realistic • Verification: • Make sure that the model implements assumptions correctly • Guidelines to follow • Look for “surprise” in output: • The simulation must follow an intuitive trend. • When possible, compare with analytical modeling. • If available, compare with real network data

15. Confidence Level Analysis

16. Confidence Level • TCP/IP simulations use some sort of random numbers. • If a terminating simulation is replicated 10 times, each with a different seed. • The question is then, how can I trust the simulations restults? • We should establish thorough statistical analysis, some level of confidence on the simulation outcome.

17. Confidence Level • Relative precision formula for 95% confidence (see Eq. 4.8, pp. 84) • where

18. Confidence Level • Usually, the relative-precision of the estimator is chosen to be 0.05 or 5% of the estimated values. • Confidence level in terminating simulation • Repeat the entire simulation many times with different random numbers (or seeds), until the relative-precision is achieved. • p105, Fig. 4.4

19. Confidence Level (cont.) • Confidence level in steady-state simulation • Fixed length simulation • Adaptive length simulation

20. Self Similar Traffic

21. Self Similar Traffic • Poisson model does not capture the burstiness of TCP/IP traffic • TCP/IP traffic usually exhibits self similar property. • In Self similar traffic, aggregating traffic over large time intervals reduces the burstiness very slowly. • Generated by superimposing many ON/OFF sources with Pareto distribution. • Paretto Distribution (a few large files, many small ones)

22. Self Similar Traffic • Using the Pareto Distribution, self-similar traffic with specific Hurst parameter can be generated in two steps: • Generate random variable, x with U(0,1). • Determine the length of an ON-OFF period as: • Where • m is the mean length of the ON-OFF period. • H is the Hurst parameter. Hurst parameter must be larger than 0.5 and smaller than 1.

23. Self Similar Traffic • The superposition of many ON-OFF sources is illustarted in Figure 4.6, where a single network queue is simulated under self-similar traffic arrival.

24. Simulation Tools

25. Classification of Simulation Tools • GPPL: General Purpose Programming Language • PSL: “Plain” Simulation Language • SP: Simulation Package • p110, Fig. 4.7

26. NS Simulator • Developed by UC Berkeley • Public domain SP • Object-oriented • Written in C++ and object-oriented tcl (Otcl) • Network components are represented by classes

27. OPNET • Developed by OPNET Technologies Inc. • Commercial SP • Object-oriented • Totally menu-driven package • Built-in model libraries contain most popular protocols and applications • Simulation task made easy

28. Selecting the Right Tool • Built-in libraries • Credibility • User-Friendliness • Technical support • Level of Details • Resource consumption • Cost

29. NS vs. OPNET • Both have emerged as de facto “standard” for simulating TCP/IP networks • P143, Table 4.6