Internet Traffic Modeling Poisson Model vs. Self-Similar Model By Srividhya Chandrasekaran Dept of CS University of Hou

1. Internet Traffic ModelingPoisson Model vs. Self-Similar ModelBySrividhya ChandrasekaranDept of CSUniversity of Houston

3. Introduction What is a model? Why do we need modeling? What are the kinds of models available? What are the models that I have discussed?

4. Poisson Model What is a Poisson Model? How does it describe Network Traffic?

6. Self-Similar Model What is Self-Similarity? What is Long Range Dependence? How does it describe Network Traffic?

8. Poisson Model vs. Self-Similar Model Poisson Model: Does not scale the Bursty Traffic properly. In fine scale, Bursty Traffic Appears Bursty, while in Coarse scale, Bursty Traffic appears smoothed out and looks like random noise.

9. Poisson Model vs. Self-Similar Model Self-Similar Model Scales Bursty traffic well, because it has similar characteristics on any scale. Gives a more accurate pictures due to Long Range Dependence in the network traffic

10. Experimental Result Researchers from UCal Berkeley, found that Poisson model could not accurately capture the network traffic.

11. Co-Existence Both the models can co-exist. In a low congestion link, Long Range Dependence characteristics are observed. As load increases, the model is pushed to Poisson. As load decreases, model pushed to Self-Similarity.

12. Remarks Two models to describe network traffic: Poisson model Self-Similar model Each has its own advantage. Both the models can co-exist to give a mode exact picture.

13. References: A Nonstationary Poisson view of Internet Traffic; TKaragiannis, M.Molle, M.Falautsos, A.Broido; Infocom in 2004 On Internet traffic Dynamics and Internet Topology II: Inter Model Validation; W.Willinger; AT&T Labs-Research Internet Traffic Tends Towards Poisson and Independent as the Load Increases; J.Cio, W.S.Cleveland, D.Lin, D.X.Sun; Nonlinear Estimation and Classification eds, 2002 On the Self-Similar Nature of Ethernet Traffic; W.Leland, M.s. Taqqu W.Willingfer, D.V.Wilson; ACM Sigcomm Proof of a fundamental Result in Self-Similar Traffic Modeling; M.S.Taqqu, W.Willinger, R.Sherman. ACMCCR: Computer Communication Review Self-Similarity; http://students.cs.byu.edu Traffic modeling of IP Networks Using the Batch Markovian Arrival Process; A.Klemm, C.Lindemann, M Lohmann; ACM 2003 Modelling and control of broadband traffic using multiplicative fractal cascades; P.M.Krishna,V.M.Gadre, U.B.Desai; IIT, Bombay

14. References Contd.. http://www.hyperdictionary.com/dictionary/stochastic+process http://www.sics.se/~aeg/report/node9.html http://www.sics.se/~aeg/report/node23.html The Effect of Statistical Multiplexing on the Long-Range Dependence of Internet Packet Traffic; Jin Cao, William S. Cleveland, Dong Lin, Don X. Su; Bell Labs Technical Report http://mathworld.wolfram.com/PoissonDistribution.html http://mathworld.wolfram.com/PoissonProcess.html http://www.itl.nist.gov/div898/handbook/eda/section3/eda366j.htm http://www.itl.nist.gov/div898/handbook/eda/section3/eda35c.htm Wide-Area Traffic: The Failure of Poisson Modeling; Vern Paxson and Sally Floyd; University of California, Berkeley Mathematical Modeling of the internet; F.Kelly, Statistical Laboratory, Univ of Cambridge. Internet Traffic modeling: Markovian Approach to self similarity traffic and prediction of Loss Probability for Finite Queues; S.Kasahara; IEICE Trans Communications, 2001

15. Questions