Bayesian Piggyback Control for Improving Real-Time Communication Quality

1. Bayesian Piggyback Control for Improving Real-Time Communication Quality Wei-Cheng Xiao1and Kuan-Ta Chen Institute of Information Science, Academia Sinica 1(Now studies in Department of Computer Science, Rice University) Presented by Yu-Chun Chang (National Taiwan University) CQR 2011 / Yu-Chun Chang

2. Outline • Motivation • Methodology: bayesian piggyback control • Performance evaluation • Summary CQR 2011 / Yu-Chun Chang

3. Real-Time Multimedia Communication Applications (1/2) • Online games • Voice chat CQR 2011 / Yu-Chun Chang

4. Real-Time Multimedia Communication Applications (2/2) • Video conferences CQR 2011 / Yu-Chun Chang

5. Motivation • Popular real-time and interactive applications • Real-time network games, VoIP • Quality of Experience CQR 2011 / Yu-Chun Chang

6. Traffic Pattern of Real-Time Communication • Real-time • Small packet size • High packet rate • User friendship • Low end-to-end delay CQR 2011 / Yu-Chun Chang

7. User Satisfaction Key Factor • Smoothness of data communication • Long end-to-end delay • Packet loss • Retransmission • Jitter • Network congestion • Packet reordering CQR 2011 / Yu-Chun Chang

8. Real-Time Communication Mechanism • Detect loss events • Retransmit loss packets CQR 2011 / Yu-Chun Chang

9. A GOOD Real-Time Communication Mechanism Should … • Work without modifying inherent network protocol and designs • Decide whether a packet has been lost before the retransmission timer expires • Avoid generating too much unnecessary traffic CQR 2011 / Yu-Chun Chang

10. Contributions • We design a packet loss event detector to • detect packet loss events without modifying protocol • determine packet loss events before retransmission timer expires • avoid unnecessary transmission overhead CQR 2011 / Yu-Chun Chang

11. Outline • Motivation • Methodology: Bayesian piggyback control • Performance evaluation • Summary CQR 2011 / Yu-Chun Chang

12. Methodology • Bayesian Piggyback Control • Bayesian inference • Probability density function estimation • Piggyback scheme Detect loss event Retransmission mechanism CQR 2011 / Yu-Chun Chang

13. Concept • Packets are transmitted via intermediate routers • Drop-tail queue CQR 2011 / Yu-Chun Chang

14. Packet Round-Trip Time • Summed up from • processing delay • propagation delay • queueing delay RTT is mainly related to this Drop-tail queue CQR 2011 / Yu-Chun Chang

15. Drop-Tail Queue FULL Enqueue Suffers the longest queueing delay Drop packet! Relay packet Tail Head CQR 2011 / Yu-Chun Chang

16. Bayesian Inference (1/2) CQR 2011 / Yu-Chun Chang

17. Bayesian Inference (2/2) CQR 2011 / Yu-Chun Chang

18. Probability Density Function Estimation • The Histogram-based method • A simple and intuitive method to estimate the conditional probability mass function • The Parzen method • A more sophisticated method to smooth the curve of the histogram-based method CQR 2011 / Yu-Chun Chang

19. The Histogram-Based Method CQR 2011 / Yu-Chun Chang

20. The Parzon Method CQR 2011 / Yu-Chun Chang

21. Detection Tradeoff • False positive rate (FPR) • An event successful is judged as lost • FPR↑: some additional traffic will be injected • False negative rate (FNR) • An event lost is judged as successful • FNR↑: will cause very high delay CQR 2011 / Yu-Chun Chang

22. Penalty Strategy CQR 2011 / Yu-Chun Chang

23. Piggyback Scheme • Previous data considered lost will be appended to construct a new packet • Retransmit loss data before transport layer timer expires • Advantages • Reduce the bandwidth requirement for packet headers • Decrease network overheads CQR 2011 / Yu-Chun Chang

24. Bayesian Piggyback Control CQR 2011 / Yu-Chun Chang

25. Outline • Motivation • Methodology: Bayesian piggyback control • Performance evaluation • Summary CQR 2011 / Yu-Chun Chang

26. Simulation Setup (1/2) • Simulator: ns2 • Network topology: transit-stub graph • 50 nodes: 1transit domain / 6 stub domains • Communication server: 1 node • Hosts running real-time applications: 15 nodes • Hosts generating cross traffic: 34 nodes CQR 2011 / Yu-Chun Chang

27. Simulation Setup (2/2) • Average bandwidth • Transit-transit domain: 2000 KB/sec • Transit-stub domain: 2000 KB/sec • Stub-stub domain: 1000 KB/sec • Real-time communication applications • Packet rate: 30 ms a packet • Packet size: 100 ~ 300 bytes • Cross traffic: UDP packets (750 KB/sec per host) CQR 2011 / Yu-Chun Chang

28. Detection Accuracy CQR 2011 / Yu-Chun Chang

29. Performance Metric • ROC (Receiver Operation Characteristics) • TPR: true positive rate • FPR: false positive rate CQR 2011 / Yu-Chun Chang

30. ROC Curve 20% 20% CQR 2011 / Yu-Chun Chang

31. Effect of Piggyback Scheme • Two kinds of delay affect user satisfaction • End-to-end delay • Lag (time difference of two contiguous message) • Performance comparison • Optimistic mechanism • Original system without any loss detection or retransmission mechanism • Pessimistic mechanism • A message will always be retransmitted until it is received CQR 2011 / Yu-Chun Chang

32. End-to-End Delay Analysis Most e2e delay values are below 1 sec. The probability is higher than 99% CQR 2011 / Yu-Chun Chang

33. Lag Analysis Our method achieve lags about only 25%-50% of those of the optimistic mechanism. CQR 2011 / Yu-Chun Chang

34. Overhead Observation

35. Summary • Demand for real-time communication applications significantly increases • Long delays degrade users’ satisfaction • Packet loss events trigger time consuming timeout retransmission mechanism • We proposed Bayesian Piggyback Control to judge packet loss events before the retransmission timer expires and retransmit loss packets efficiently (with few overheads) • The proposed detector achieves at least 80%detection rate as the false alarm below 20% CQR 2011 / Yu-Chun Chang

36. Thank you for your attention! CQR 2011 / Yu-Chun Chang