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Can Internet Video-on-Demand Be Profitable?

Can Internet Video-on-Demand Be Profitable?. Cheng Huang, Jin Li (Microsoft Research), Keith W. Ross (Polytechnic University) ACM SIGCOMM 2007. Outlines. Motivation Trace – User demand & behavior Peer assisted VoD Theory Real-trace-driven simulation Cross ISP traffic issue Conclusion.

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Can Internet Video-on-Demand Be Profitable?

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  1. Can Internet Video-on-Demand Be Profitable? Cheng Huang, Jin Li (Microsoft Research), Keith W. Ross (Polytechnic University) ACM SIGCOMM 2007

  2. Outlines • Motivation • Trace – User demand & behavior • Peer assisted VoD • Theory • Real-trace-driven simulation • Cross ISP traffic issue • Conclusion

  3. Motivation • Saving money for huge content providers such as MS, Youtube • Video quality is just acceptable User BW ++++++ User BW + User BW +++ User demand +++ Traffic ++ Traffic + Traffic +++ Traffic ++++++++ ISP Charge + ISP Charge +++++++ ISP Charge ++ ISP Charge +++ P2P Client Server Video quality +++ Video quality +++ Video quality + Video quality +++++++

  4. P2P Architecture • Peers will assist each other and won’t consume the server BW • Each peer have contribution to the whole system • Throw the ball back to the ISPs • The traffic does not disappear, it moved to somewhere else

  5. Outlines • Motivation • Trace – User demand & behavior • Peer assisted VoD • Theory • Real-trace-driven simulation • Cross ISP traffic issue • Conclusion

  6. Trace Analysis • Using a trace contains 590M requests and more than 59000 videos from Microsoft MSN Video (MMS) • From April to December, 2006

  7. Video Popularity • The more skewed, the much better

  8. Download bandwidth • Use • ISP download/upload pricing table • Downlink distribution to generate upload bw distribution

  9. Demand V.S. Support

  10. User behavior - Churn

  11. User Behavior - Interaction

  12. Content quality revolution

  13. Traffic Evolution 1.23 2.27 Quality Growth: 50% User Growth: 33% Traffic Growth: 78.5%

  14. Outlines • Motivation • Trace – User demand & behavior • Peer assisted VoD • Theory • Real-trace-driven simulation • Cross ISP traffic issue • Conclusion

  15. P2P Methodologies • Users arrive with poison distribution • Exhaustive search for available upload BW Video rate: 60 60 70 Total Demand 60 x 4 = 240 100 40 0 30 10 0 Total Support 100+40+30+100 = 270 40 0 100

  16. System status • IfSupport >Demand • Surplus mode, small server load • IfSupport<Demand • Deficit mode, VERY large server load • IfSupport≈Demand • Balanced mode, medium server load

  17. Prefetch Policy • When the system status vibrates between surplus and deficit mode • Let every peer get more video data than demand (if possible) in surplus mode • And thus they can tide over deficit phase

  18. Outlines • Motivation • Trace – User demand & behavior • Peer assisted VoD • Theory • Real-trace-driven simulation • Cross ISP traffic issue • Conclusion

  19. Methodology • Event-based simulator • Driven by 9 months of MSN Video trace • Use greedy prefetch for P2P-VoD • For each user i, donate it’s upload BW and aggregated BW to user i+1 • If user i’s buffer point is smaller than user i+1’s • BW allocate to user i+1 is no more than user i

  20. Trace-driven simulationLevel • Non-early-departure Trace • Non-user-interaction Trace • Full Trace

  21. Simulation: Non-early-departure

  22. Simulation: Early departure (No interaction) • When video length > 30mins, 80%+ users don’t finish the whole video

  23. Simulation: Full • How to deal with buffer holes • As user may skip part of the video • Two strategies • Conservative: Assume that user BW=0 after the first interaction • Optimistic: Ignore all interactions

  24. Results of full trace simulation (1/2)

  25. Results of full trace simulation (2/2)

  26. Outlines • Motivation • Trace – User demand & behavior • Peer assisted VoD • Theory • Real-trace-driven simulation • Cross ISP traffic issue • Conclusion

  27. ISP-unfriendly P2P VoD • ISPs, based on business relations, will form economic entities • Traffic do not pass through the boundary won’t be charged • ISP-unfriendly P2P will cause large amount of traffic

  28. Simulation results of unfriendly P2P

  29. Simulation results of friendly P2P • Peers lies in different economic entities do not assist each other

  30. Conclusion (Pros) • This paper gives a representative trace analysis that breaks the myth of upload BW problems • Successfully address the importance of the P2P cross-ISP problem

  31. Conclusions (Cons) • Weak and unrealistic P2P models • Unclear comparisons between each P2P strategies and simulations

  32. Thank You

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