1. WBest: a Bandwidth Estimation Tool for IEEE 802.11 Wireless Networks Presented by Feng Li (lif@cs.wpi.edu)lif@cs.wpi.edu Mingzhe Li, Mark Claypool, and Robert Kinicki {lmz, claypool, rek}@cs.wpi.edu Department of Computer Science, Worcester Polytechnic Institute, Worcester MA, 01609 USA 33rd IEEE Conference on Local Computer Networks (LCN), Montreal, Quebec, Canada, October 16 th,2008

2. LCN08 – October 16 th, Montreal, Quebec, Canada 2 Motivation  Bandwidth estimation techniques focus on network capacity or available bandwidth.  Most bandwidth estimation involved only wired networks.  This paper presents a new Wireless Bandwidth estimation tool, WBest, designed for fast, non- intrusive, accurate estimation of available bandwidth over wireless LANs.

3. LCN08 – October 16 th, Montreal, Quebec, Canada 3 Challenges on Bandwidth Estimation  Traditional approaches. (e.g. pathChirp v2.4.1 [Ribeiro 2003], pathload v1.3.2 [Jain 2003] etc.) –Designed for precisely estimate the bandwidth in wired networks. –Converge based on searching algorithms. –Provide limited bandwidth information.  Impacted by wireless networks. (e.g. shared media, retransmission, interference etc), –Inaccurate results. –Long estimation time. –High intrusiveness.

4. LCN08 – October 16 th, Montreal, Quebec, Canada 4 Capacity Estimation with Packet Dispersion Bottleneck router L :Packet size C i :Bottleneck capacity ∆ in :Initial gap ∆ out : Dispersed gap

5. LCN08 – October 16 th, Montreal, Quebec, Canada 5 Example: Packet Dispersion with Wireless Contention Probing traffic Contending traffic / Co-channel interference

6. LCN08 – October 16 th, Montreal, Quebec, Canada 6 Outline  Motivation and Backgrounds  WBest Algorithm  Evaluation Experiments  Result Analysis  Conclusions

7. LCN08 – October 16 th, Montreal, Quebec, Canada 7 Terminology  Effective Capacity (C e ) –Maximum possible bandwidth that a link or end-to-end path can deliver.  Available Bandwidth (A ) –Maximum unused bandwidth at a link or end-to-end path in a network. –Typically, it is a time-varying metric.

8. LCN08 – October 16 th, Montreal, Quebec, Canada 8 Wireless Bandwidth Estimation Tool (WBest)  Objective –Fast, low intrusiveness, adequately accurate estimation of available bandwidth and variance of bandwidth in wireless networks.  Two-step algorithm –Packet pair technique to estimate effective capacity (C e ) of wireless network. –Packet train technique to estimate mean and standard deviation of available bandwidth (A).

9. LCN08 – October 16 th, Montreal, Quebec, Canada 9 WBest Assumptions  Assume last hop wireless network (hth hop) is bottleneck link with a single FCFS queue and:  Assume no significant changes in network conditions between two steps (estimating C e and A).

10. LCN08 – October 16 th, Montreal, Quebec, Canada 10 Estimating Effective Capacity (C e )  Send n packet pairs to estimate C e : –T i : dispersion time of ith packet pair (seconds), –L : packet size (bytes).  Use median of n estimations to minimize impacts of crossing and contending traffic.

11. LCN08 – October 16 th, Montreal, Quebec, Canada 11 Estimating Available Bandwidth (A)  A packet train of m packets is sent at effective capacity (C e ) to estimate available bandwidth (A).  FCFS queuing at AP. ─ R : dispersion rate S : crossing/contending traffic ─ S’ : reduced crossing/contending traffic  Estimate contending and crossing traffic (S) using dispersion rate (R)

12. LCN08 – October 16 th, Montreal, Quebec, Canada 12 Estimating Available Bandwidth (A) (cont’d)  Mean available bandwidth (A). Fig 3 Estimating Available Bandwidth using Average Dispersion Rate (R).

13. LCN08 – October 16 th, Montreal, Quebec, Canada 13 WBest Algorithm 2 nd Phase Calculating A Error Correction 1 st Phase Calculating C e m = 30 n = 30

14. LCN08 – October 16 th, Montreal, Quebec, Canada 14 Outline  Motivation and Background  WBest Algorithm  Evaluation Experiments  Result Analysis  Conclusions

15. LCN08 – October 16 th, Montreal, Quebec, Canada 15 Evaluation Setup  Build testbed – Open source drivers – Wireless sniffer  Various wireless conditions – Traffic load – Power saving mode – Rate adaptation  Implementation of WBest  Compare with: – IGI/PTR v2.0 [Hu 2003] (PGM/PRM) – pathChirp v2.4.1 [Ribeiro 2003] (PRM) – pathload v1.3.2 [Jain 2003] (PRM) Client C

16. LCN08 – October 16 th, Montreal, Quebec, Canada 16 Experiment Design  14 cases were designed to evaluate four bandwidth estimation tools under different network conditions.  Each of 14 cases were repeated 30 times.  All clients were placed with pre-selected locations with RSSI range between -38 and -42 dBm.  All experiments were run during summer break to eliminate effects from occasional wireless activities.

17. LCN08 – October 16 th, Montreal, Quebec, Canada 17 Result-Convergence Time vs. Error

18. LCN08 – October 16 th, Montreal, Quebec, Canada 18 Result-Intrusiveness vs. Error

19. LCN08 – October 16 th, Montreal, Quebec, Canada 19 Future Work  Apply WBest to multimedia streaming applications to improve media performance and playout buffer optimization on wireless networks.  Evaluate WBest performance under more complex wireless environments.  Enhance WBest robustness during AP queue overflow.  Develop new metric to replace Available Bandwidth (A) when TCP flows involved.

20. LCN08 – October 16 th, Montreal, Quebec, Canada 20 Conclusions  Current bandwidth estimation tools are significantly impacted by wireless network conditions, such as contention or rate adaptations.  Current tools are generally impractical for applications such as streaming multimedia that require fast, accurate and low intrusive bandwidth estimation.  WBest consistently provides fast available bandwidth estimation, with generally more accurate estimates and lower intrusiveness under all conditions evaluated.

21. LCN08 – October 16 th, Montreal, Quebec, Canada 21 Question ?  WBest with source code is available at: http://perform.wpi.edu/downloads/#wbest

22. LCN08 – October 16 th, Montreal, Quebec, Canada 22 Thank You! WBest: a Bandwidth Estimation Tool for IEEE 802.11 Wireless Networks Presented by Feng Li (lif@cs.wpi.edu)lif@cs.wpi.edu Mingzhe Li, Mark Claypool, and Robert Kinicki {lmz, claypool, rek}@cs.wpi.edu Department of Computer Science, Worcester Polytechnic Institute, Worcester MA, 01609 USA

23. LCN08 – October 16 th, Montreal, Quebec, Canada 23 Reference  [Hu 2003] Ningning Hu and Peter Steenkiste, “Evaluation and characterization of available bandwidth probing techniques,” IEEE Journal on Selected Areas in Communications, vol. 21, no. 6, Aug. 2003.  [Ribeiro 2003] V. Ribeiro, R. Riedi, R. Baraniuk, J. Navratil, and L. Cottrell, “pathchirp: Efficient available bandwidth estimation for network paths,” in PAM ’03, La Jolla, CA, USA, Apr. 2003.  [Jain 2003] Manish Jain and Constantinos Dovrolis, “End-to- end available bandwidth: Measurement methodology, dynamics, and relation with tcp throughput,” IEEE/ACM Transactions in Networking,, no. 295-308, Aug. 2003.

24. LCN08 – October 16 th, Montreal, Quebec, Canada 24 Analysis of Number of Packet Pairs

25. LCN08 – October 16 th, Montreal, Quebec, Canada 25 Analysis of Length of Packet Train