Workload Generation for ns. Simulations of Wide Area Networks
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1 1 Workload Generation for ns Simulations of Wide Area Networks and the Internet 1 M. Yuksel y, B. Sikdar z K. S. Vastola z and B. Szymanski y y Department of Computer Science z Department of Electrical Computer and System Engineering Rensselaer Polytechnic Institute Troy, NY USA fyuksem,bsikdar,vastolag@networks.ecse.rpi.edu, szymansk@cs.rpi.edu Ph: This work supported by DARPA under contract number F C-0057.
2 2 Outline of the talk The ns network simulator Simulating wide area networks: Issues { Trac composition and protocol dierences { Trac generation { Topology Simulating wide area networks: Solutions { Trac composition { Trac generation Simulation results Summary and conclusions
3 3 Simulation platform: ns ns: Network simulator developed by UCB /LBLN and the VINT project Open source code and particularly easy to modify Includes libraries of topology and trac generators and visualization tools Wide acceptance in the networking research community
4 4 Simulating wide area networks: Issues Trac composition and protocol dierences { Simulated trac should maintain the proper composition { Sources select their destinations randomly { Dierent applications generate sessions with dierent distributions { Number of sessions in a network varies continuously { ns is incapable of accounting for these eects
5 5 Simulating wide area networks: Issues (Cont.) Trac generation { Trace based trac generators: Unable to account for changing network conditions and feedback { Source based trac generators { ns does not use application specic stochastic models to generate trac { ns does not have generators for long-range dependent (Self-similar) network trac
6 6 Simulating wide area networks: Issues (Cont.) Topology issues { WANs and the Internet can be viewed as a diverse collection of interconnected domains { Each domain has its own internal topology { Large networks also have wide variations in their link bandwidths { ns has libraries for topology generation
7 7 Simulating wide area networks: Solutions Trac composition { We have implemented mechanisms to control the percentage of TCP and UDP sessions in the trac { Our implementation randomly chooses sourcedestination pairs from the nodes in the network { Capability to use any suitable distribution to characterize workload division between nodes and use it to select the source-destination pairs
8 8 Simulating wide area networks: Solutions (Cont.) Session generation { We introduce application specic trac generators in ns for Telnet, WWW, FTP and SMTP { We use empirical distributions to characterize the inter-arrival times, duration and data transferred by each application { Sessions generated by each application characterized by mean number of sessions (MNS) mean inter-arrival time of sessions (MIATS) mean duration time of sessions (MDTS)
9 9 Simulating wide area networks: Solutions (Cont.) Session generation { Each expiring session replaced by random number of sessions { Continuous variation in the number of active sessions giving a more realistic network scenario
10 Simulating wide area networks: Solutions (Cont.) 10 Trac generation: Self-similar trac generators { Long-range dependence in aggregated WWW, Ethernet and WAN trac { We implemented two self-similar trac generators in ns { Application/Traffic/SupFRP: Based on superposition of Fractal renewal processes { Application/Traffic/SS: Based on superposition of Markov modulated Poisson processes
11 11 Simulation results: Mean duration times of sessions Mean Duration Time (Seconds) Telnet WWW FTP SMTP Simulated Time (Hours) Mean number of sessions for various application as a function of the simulation length The mean duration times of sessions converges to the desired value (50 sec) within a short simulation time
12 12 Simulation results: Mean number of sessions Mean Number of Sessions Telnet WWW FTP SMTP Simulated Time (Hours) Mean number of sessions for various application as a function of the simulation length The continuous variation of the number of sessions results in a closer model of real networks
13 Simulation results: Self-similar trac generators Covariance (SupFRP) H = H H = 0.90 = Poisson k (lag) Variance-lag plot for the trac generated SupFRP compared with a Poisson process We note that the correlations decay extremely slowly and follow a power law characteristic of self-similar processes
14 Simulation results: Self-similar trac generators Covariance (SS) H = H H = 0.90 = Poisson k (lag) Variance-lag plot for the trac generated SS compared with a Poisson process We note that the correlations decay extremely slowly and follow a power law characteristic of self-similar processes
15 15 Summary and conclusions We presented a methodology for generating realistic workloads for WANs using the network simulator ns Our method captures the temporal and spatial correlation in network trac We created tools to generate trac specic to applications like Telnet, FTP, WWW and SMTP Implemented two accurate self-similar traf- c generators in ns to simulate long-range dependent aggregate trac
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