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Modeling of Aggregate Available Bandwidth in Many-to-One Data Transfer

Modeling of Aggregate Available Bandwidth in Many-to-One Data Transfer. S. C. Hui and Jack Y. B. Lee Department of Information Engineering The Chinese University of Hong Kong, Hong Kong. Content. Background Bandwidth Availability Multiple-source Bandwidth Availability Applications

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Modeling of Aggregate Available Bandwidth in Many-to-One Data Transfer

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  1. Modeling of Aggregate Available Bandwidth in Many-to-One Data Transfer S. C. Hui and Jack Y. B. Lee Department of Information Engineering The Chinese University of Hong Kong, Hong Kong

  2. Content • Background • Bandwidth Availability • Multiple-source Bandwidth Availability • Applications • Performance Evaluation • Summary and Future Work Presentation for AOE meeting : Modeling of Aggregate Available Bandwidth in Many-to-One Data Transfer

  3. Internet ? ? ? Background • The Internet • On a global scale it is still best-effort without end-to-end QoS guarantee. • Presents challenges to bandwidth-sensitive applications (eg. Video streaming) Client Media Server Playback audio/video storage I/O and CPU Allocation and Scheduling I/O Resources Allocation and Scheduling End-to-end QoS guarantee Presentation for AOE meeting : Modeling of Aggregate Available Bandwidth in Many-to-One Data Transfer

  4. Media Server storage Background • Employing multiple senders has the benefits of • Increasing the throughput • Adapting the network bandwidth variation • Reducing bursty packet loss • Modeling the aggregate bandwidth Media Server storage Client Playback audio/video Internet …… Presentation for AOE meeting : Modeling of Aggregate Available Bandwidth in Many-to-One Data Transfer

  5. Bandwidth Availability • Experiments in PlanetLab [1] • Measure the bandwidth availability of 47 hosts using Iperf [2]. PlanetLab Hosts (sender) PlanetLab Hosts (receiver) s1 TCP TCP s2 r Internet . . . TCP Throughput - averaged over 10s interval - total 3 hours s47 Presentation for AOE meeting : Modeling of Aggregate Available Bandwidth in Many-to-One Data Transfer

  6. Bandwidth Availability • Experiments in PlanetLab • Average bandwidth from 0.04 to 4.53 Mbps and CoV from 0.16 to 0.88. Presentation for AOE meeting : Modeling of Aggregate Available Bandwidth in Many-to-One Data Transfer

  7. Bandwidth Availability • Experiments in PlanetLab • Bandwidth distributions: Presentation for AOE meeting : Modeling of Aggregate Available Bandwidth in Many-to-One Data Transfer

  8. Bandwidth Availability • Observations for one-to-one case • Both mean bandwidth and CoV vary substantially across different senders • Their distributions do not conform consistently Presentation for AOE meeting : Modeling of Aggregate Available Bandwidth in Many-to-One Data Transfer

  9. Multiple-source Bandwidth Availability • Experiments in PlanetLab • Measure the aggregate bandwidth availability of multiple senders. PlanetLab Hosts (sender) PlanetLab Hosts (receiver) s1 TCP TCP s2 r Internet . . . TCP Throughput - averaged over 10s interval - total 3 hours s47 Presentation for AOE meeting : Modeling of Aggregate Available Bandwidth in Many-to-One Data Transfer

  10. Multiple-source Bandwidth Availability • Experiments in PlanetLab • Half of the sender-pairs have correlation coefficient < 0.2 • All sender-pairs have correlation coefficients < 0.6. Presentation for AOE meeting : Modeling of Aggregate Available Bandwidth in Many-to-One Data Transfer

  11. Multiple-source Bandwidth Availability • Experiments in PlanetLab • Bandwidth distribution (sum of 47 senders): Measured data Normal distribution with same mean & variance Presentation for AOE meeting : Modeling of Aggregate Available Bandwidth in Many-to-One Data Transfer

  12. Multiple-source Bandwidth Availability • Experiments in PlanetLab • Normal conformance versus number of senders: • p-value computed using the Shapiro-Wilk test [3] A p-value of 0.05 or higher is considered conform to normal distribution. Presentation for AOE meeting : Modeling of Aggregate Available Bandwidth in Many-to-One Data Transfer

  13. Multiple-source Bandwidth Availability • Observations for one-to-one case • Both mean bandwidth and CoV vary substantially across different senders • Their distributions do not conform consistently • Observations for many-to-one case • The aggregate available bandwidth of multiple senders tends to be normally-distributed. • Works for as few as 4 senders (in our experiment). • These are end-to-end available bandwidth • Already accounted for the effect of link capacity, competing traffics, variations in the sender itself, dynamics of the transport protocol (TCP), etc. Presentation for AOE meeting : Modeling of Aggregate Available Bandwidth in Many-to-One Data Transfer

  14. Applications • Video Content Distribution over best-effort networks (Internet) Presentation for AOE meeting : Modeling of Aggregate Available Bandwidth in Many-to-One Data Transfer

  15. Applications • Video Content Distribution over best-effort networks (Internet) Presentation for AOE meeting : Modeling of Aggregate Available Bandwidth in Many-to-One Data Transfer

  16. Applications • Video Content Distribution over best-effort networks (Internet) Presentation for AOE meeting : Modeling of Aggregate Available Bandwidth in Many-to-One Data Transfer

  17. Applications • Bandwidth available << Video bit-rate • Streaming not possible • Download takes very long and unpredictable amount of time • Playback before download possible but cannot guarantee playback continuity Presentation for AOE meeting : Modeling of Aggregate Available Bandwidth in Many-to-One Data Transfer

  18. Applications • Bandwidth available << Video bit-rate • Hybrid Download-Streaming • The goal is to shorten w and ensure playback, once started, will be continuous. Partial download (w intervals) Time (Divides the time into intervals) Playback begins after w intervals. Presentation for AOE meeting : Modeling of Aggregate Available Bandwidth in Many-to-One Data Transfer

  19. Cj is normally distributed (F()) Hybrid Download-Streaming Partial download (w intervals) (Divides the time into intervals) Time Playback begins after w intervals. Let the CBR movie bit rate : R Let aggregate data transfer rate for interval i: Ci Total data received by interval i : Total data played back by interval i : To ensure continuous playback we need: Presentation for AOE meeting : Modeling of Aggregate Available Bandwidth in Many-to-One Data Transfer

  20. Hybrid Download-Streaming Partial download (w intervals) (Divides the time into intervals) Time Playback begins after w intervals. Given the acceptable probability for failing continuous playback Δ To guarantee smooth playback with the probability Δ, So we need a partial download time of : n-times autoconvolution of Cj’s CDF Presentation for AOE meeting : Modeling of Aggregate Available Bandwidth in Many-to-One Data Transfer

  21. Hybrid Download-Streaming • Trace-driven Simulation • Bandwidth traces from PlanetLab. • 5 ~ 10 senders, video length from 500 to 1,000 seconds, bit-rate ranges from 200 ~ 300 kbps. • Comparison • Pure download – download the entire video before playback. • Hybrid download-streaming. • Lower bound – computed using a priori knowledge of future bandwidth availability (i.e., all Ci’s are known). Presentation for AOE meeting : Modeling of Aggregate Available Bandwidth in Many-to-One Data Transfer

  22. Hybrid Download-Streaming • Simulation Results Pure Download Hybrid Download-Streaming Lower Bound Presentation for AOE meeting : Modeling of Aggregate Available Bandwidth in Many-to-One Data Transfer

  23. Applications • Video Content Distribution over best-effort networks (Internet) Presentation for AOE meeting : Modeling of Aggregate Available Bandwidth in Many-to-One Data Transfer

  24. Applications • Bandwidth available ≈ Video bit-rate • Conventional streaming may not work well due to bandwidth fluctuations. • Additional pre-buffering is needed (e.g., hybrid download-streaming) • However since the available bandwidth is close to the video bit-rate, can we accommodate the bandwidth fluctuations without additional startup delay? Presentation for AOE meeting : Modeling of Aggregate Available Bandwidth in Many-to-One Data Transfer

  25. Applications • Bandwidth available ≈ Video bit-rate • Playback-adaptive Video Streaming Video – vary display frame rate Audio – vary sampling rate or use time-scale modification s1 s2 Playback TCP TCP r Internet . . . s47 {Ti’s} {Ci’s} If the playback rate adjustment a is small (e.g., a  5%) it will not be noticeable. Presentation for AOE meeting : Modeling of Aggregate Available Bandwidth in Many-to-One Data Transfer

  26. Playback-adaptive Video Streaming Let the number of senders : N Let the CBR movie bit rate : R Let the bandwidth availability is taken at intervals of T sec The total amount of data received from all N senders at time interval j : The amount of data consumed at interval j : Amount of Data received from sender i at interval j Presentation for AOE meeting : Modeling of Aggregate Available Bandwidth in Many-to-One Data Transfer

  27. Playback-adaptive Video Streaming The client buffer occupancy at interval j : By adjusting the playback rate within a small range, α, the playback time intervals can be adjusted : Data reception rate at interval j : Data consumption rate at interval j : Presentation for AOE meeting : Modeling of Aggregate Available Bandwidth in Many-to-One Data Transfer

  28. Playback-adaptive Video Streaming Let Bj be the actual buffer occupancy at interval j. The estimated buffer occupancy at next interval : The goal is to maintain buffer occupancy level, i.e. Given the client can tolerate a probability of Δ of failing this constraint, Possible X : Prebuffer level Presentation for AOE meeting : Modeling of Aggregate Available Bandwidth in Many-to-One Data Transfer

  29. Playback-adaptive Video Streaming • Rebuffering • Instead using fixed rebuffer size, we calculate the rebuffer size as Presentation for AOE meeting : Modeling of Aggregate Available Bandwidth in Many-to-One Data Transfer

  30. Playback-adaptive Video Streaming • Trace-driven Simulation • 5 senders, 5 seconds initial pre-buffer. • Video bit-rate = Avg available bandwidth (R=E[Ci]) • Metric : Avg number and duration of playback interruptions • Comparison • Conventional streaming without adaptation, pause and rebuffer 5 seconds video data whenever buffer underflows. • Bandwidth modeling with adaptive playback, pause and rebuffer 5 seconds video data whenever buffer underflows. • Bandwidth modeling with adaptive playback and adaptive rebuffering - compute the rebuffer size based on the estimated bandwidth model. Presentation for AOE meeting : Modeling of Aggregate Available Bandwidth in Many-to-One Data Transfer

  31. Playback-adaptive Video Streaming • Simulation Results Conventional streaming Adaptive playback & rebuffering Adaptive playback only Presentation for AOE meeting : Modeling of Aggregate Available Bandwidth in Many-to-One Data Transfer

  32. Playback-adaptive Video Streaming • Simulation Results Conventional streaming Adaptive playback only Adaptive playback & rebuffering Presentation for AOE meeting : Modeling of Aggregate Available Bandwidth in Many-to-One Data Transfer

  33. Playback-adaptive Video Streaming • Simulation Results (α = 5%) Number of occurrences Duration Presentation for AOE meeting : Modeling of Aggregate Available Bandwidth in Many-to-One Data Transfer

  34. Summary and future work • Initial Results • Based on experiments conducted in PlanetLab • With 4 or more senders the aggregate available end-to-end bandwidth is normally-distributed. • The aggregate bandwidth availability is stable for many hours. • The estimated bandwidth model can be used for admission control and playback scheduling to improve the quality of service. Presentation for AOE meeting : Modeling of Aggregate Available Bandwidth in Many-to-One Data Transfer

  35. Summary and future work • Open Problems • Validity of the model in the broader Internet • How many senders are needed? • Challenges in parameter estimation. • How long does the measured parameter remain correct? • What if some senders are correlated, e.g., pass through the same network bottleneck to the receiver? • What if the network topology is known or partially known? • Applications • Peer-to-peer applications, distributed servers, etc. Presentation for AOE meeting : Modeling of Aggregate Available Bandwidth in Many-to-One Data Transfer

  36. References Cited Work • PlanetLab, URL: http://www.planet-lab.org/ • IPerf, URL: http://dast.nlanr.net/Projects/Iperf/ • Hahn and Shapiro, Statistical Models in Engineering, Wiley, 1994. Publications • S. C. Hui and Jack Y. B. Lee, “Modeling of Aggregate Available Bandwidth in Many-to-One Data Transfer,” Proc. of the Fourth International Conference on Intelligent Multimedia Computing and Networking, July 21-26, 2005, Utah, USA. • S. C. Hui and Jack Y. B. Lee, “Playback-Adaptive Multi-Source Video Streaming,” Proc. of the Fourth International Conference on Intelligent Multimedia Computing and Networking, July 21-26, 2005, Utah, USA. Presentation for AOE meeting : Modeling of Aggregate Available Bandwidth in Many-to-One Data Transfer

  37. ~ End of Presentation ~ Presentation for AOE meeting : Modeling of Aggregate Available Bandwidth in Many-to-One Data Transfer

  38. Correlation Coefficient The correlation coefficient is a scaled version of the covariance between two users: where Presentation for AOE meeting : Modeling of Aggregate Available Bandwidth in Many-to-One Data Transfer

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