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

1 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 Web Mail P2P Etc.

2 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

3 LiTGen’s underlying model SESSION IS OBJECT PACKETS: T IS IA OBJ N SESSION N OBJ IA PKT

4 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: IA pkt = f(N obj )

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

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

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

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

9 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 ?

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

11 SE results: mail traffic Captured trace Synthetic trace

12 SE results: P2P traffic Captured trace Synthetic trace

13 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

14 Mail traffic sensitivity Insensitive distributions Sensitive distributions

15 P2P traffic sensitivity Insensitive distributions Sensitive distributions

16 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

17 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

18 Thank you !

19 Trace originating on the Sprint access network