1 / 32

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.

dalmar
Télécharger la présentation

Can Internet Video-on-Demand Be Profitable?

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript

  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

More Related