LiTGen, a lightweight traffic generator: application to mail and P2P wireless traffic

1. LiTGen, a lightweight traffic generator: application to mail and P2P wireless traffic • Chloé Rolland*, Julien Ridoux+ and Bruno Baynat* • * Laboratoire LIP6 – CNRS • Université Pierre et Marie Curie – Paris 6 • + ARC Special Research Center for Ultra-Broadband Communications (CUBIN), • The University of Melbourne

2. Web Mail P2P Etc. Generating IP traffic with accurate timescales properties • General framework: multiple applications • LiTGen, a lightweight traffic generator • Semantically meaningful structure • Does not rely on a network and/or TCP emulator • Fast computation • Measurement based validation • Application to mail and P2P wireless traffic

3. LiTGen’s underlying model • Focus on the download path • Do not consider up/down interactions • Focus on TCP traffic • Approach • Application oriented & User oriented • Semantically meaningful hierarchical model

4. SESSION SESSION OBJECT OBJECT OBJECT TIS NSESSION IAOBJ IAOBJ NOBJ IAPKT LiTGen’s underlying model IS PACKETS:

5. Basic vs. Extended LiTGen • Basic LiTGen • Renewal processes • Successive random variables (R.V.) i.i.d. • No dependency between different R.V. • Extended LiTGen • Renewal processes • Dependency introduced, the average packets inter-arrival depends on the objects size: IApkt = f(Nobj)

6. User filter Download traffic Mail traffic Application filter src port select. Sessions Objects Mail traffic to user 2 Mail traffic to user i Mail traffic to user 1 Mail traffic to user i Objects id. Session id. Calibration by inspection of the wireless trace • Wireless trace: US ISP wireless network

7. Validation methodology • Wavelet analysis of the packets arrival times series (LDE) Energy spectrum comparison ? Captured trace Synthetic trace

8. Comparison of different kinds of traffics spectra (1/2) Web + Mail + P2P traffic

9. Comparison of different kinds of traffics spectra (2/2) Mail traffic P2P traffic

10. Further validation: semi-experiments (SE) • Does LiTGen reproduces the traffic internal structure? • Semi-experiments • Manipulation of internal parameters • Impact of the manipulation: importance of the parameters modified ?

11. 1. Impact ? 2. Similar reaction ? Example of SE: P-Uni • Uniformly distributes packets arrival times within each object • Examine impact of in-objects packets burstiness P-Uni Captured trace P-Uni Synthetic trace

12. SE results: mail traffic Captured trace Synthetic trace

13. SE results: P2P traffic Captured trace Synthetic trace

14. Traffic sensitivity with regards to the distributions • Random Variables (R.V.) distributions? • Heavy-tailed distributions important? • Source of correlation in traffic? • Investigation of each R.V. separately • Replace individually the empirical distribution of the studied R.V. by a memoryless distribution • Model the other R.V. by the empirical distributions • Impact on the spectra? • Conclusion on the importance of the R.V. distribution

15. Mail traffic sensitivity Sensitive distributions Insensitive distributions

16. P2P traffic sensitivity Insensitive distributions Sensitive distributions

17. Conclusion • Extended LiTGen reproduces accurately the traffic scaling properties • Investigation of the impact of the R.V. distributions • The in-objects organization is crucial • Heavy-tailed distribution correlation • Give insights for the development of accurate traffic models

18. Future works • Dependency introduced in Extended LiTGen • Realistic performance prediction? • Burstiness: strong implications on queuing & performance • Compare the performance of a model fed by • The captured traffic • The synthetic traffic from LiTGen • Simpler renewal processes

19. Thank you !

20. Trace originating on the Sprint access network