Speaker: Yu-Fu Huang Advisor: Dr. Kai-Wei Ke Date : 2014, Mar. 17

1. A page-oriented WWW traffic model for wireless system simulations Speaker: Yu-Fu Huang Advisor: Dr. Kai-Wei Ke Date : 2014, Mar. 17

2. Outline • Traffic models from Poisson to Self-Similar • WWW Traffic structure • Web traffic characterization • Simulation and results • Conclusion • Reference

3. The interest towards traffic model • Traffic models are needed as input in network simulation. • A good traffic model may lead to a better understanding of the characteristics of the network traffic itself.

4. Stochastic Counting Process X2 X1 X3 X4 X7 X5 X6 t • Poisson process ⊆ Renewal process • Independent increment • Memoryless property • Inter-arrival time pdf: Exponential • Renewal process • Independent increment • Inter-arrival time pdf: Arbitrary T=X1+X2+X3+X4+… X1,X2,X3… are i.i.d Poisson process: - Any point in the time axis meets Memoryless property. Renewal process: - Only point exactly at exiting one period and entering a new period meets Memoryless property.

5. Variance of sample mean approaches to zero as n approaches to infinite.

8. Traffic models from Poisson to Self-Similar • Self-Similar process • Long Range Dependency • Infinite Variance

11. Heavy-tailed probability distribution

12. Outline • Traffic models from Poisson to Self-Similar • WWW Traffic structure • Web traffic characterization • Simulation and results • Conclusion • Reference

13. WWW Traffic structure • Two approaches to data traffic modelling: • Behaviorist or black-box approach: • Modelled w/o taking into account the causes that lead to them • Structure approach: • Model design is based on the internal structure of traffic generating system

18. Outline • Traffic models from Poisson to Self-Similar • WWW Traffic structure • Web traffic characterization • Simulation and results • Conclusion • Reference

20. Pages per session

23. Time between pages

26. Page size

28. Heavy-tailed probability distribution

29. Packet size

30. Packet inter-arrival time Page Packet PIT

31. Outline • Traffic models from Poisson to Self-Similar • WWW Traffic structure • Web traffic characterization • Simulation and results • Conclusion • Reference

32. Test conditions 2000s of average session interarrival time Constant service rate of 2 KBps (I) 4MB Queue (II) 82.75s of average session interarrival time Test condition (I): With proposed model adapted to corporate environment  Server utilization rate: 68% With ETSI model adapted to corporate environment  Server utilization rate: 3% Test condition (II): With proposed model adapted to corporate environment  Server utilization rate: 68% With ETSI model adapted to corporate environment but increasing average session interarrival time from 2000s to 82.75s Get server utilization rate: 68% Adjusted Utilization ESTI model

33. ESTI Model

34. Test condition (I) Test condition (II)

35. Conclusions (I) • Traffic models summary: • Independent interarrival time: Exponential • Session or packet interarrival • Cumulative independent interarrival time: Gamma or Erlang distribution • Page interarrival • Data size: Self-similar distribution • Page size

36. Conclusions (II) • ESTI model underestimates packet losses and delay in a queue due to the low load offered by the ESTI model. • The proposed model generates a traffic load similar to the measured one and much more burstiness than the ESTI one.

37. Reference [1] Reyes-LecuonaA., González-Parada E., and Díaz-Estrella A., “A page-oriented WWW traffic model for wireless system simulations” Proceedings of the 16th International TeletrafficCongress (ITC16), Edinburgh, United Kindom, pp. 1271-1280, June 1999. [2] Staehle D., Leibnitz K., and Tran-Gia P., “Source Traffic Modeling of Wireless Application” InstitutfürInformatik, WürzburgUniversität, Technical Report No. 261, June 2000. [3] MichelaBecchi, “From Poisson Process to Self-Similarity: a Survey of Network Traffic Models” mbecchi@wustl.edu.