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Can Internet Video-on-Demand be Profitable?

Can Internet Video-on-Demand be Profitable?. Cheng Huang, Jin Li (Microsoft Research Redmond), 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

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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 Redmond), 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 MSN Video, Youtube, Yahoo Video, Google Video,… • 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 520M streaming requests and more than 59,000+ videos from Microsoft MSN Video. • http://video.msn.com/ • From April to December, 2006.(9 Months)

  7. The popularity distributions are quite similar. • There is indeed a high-degree of locality. • The distribution is more skewed than a Zipf distribution. 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 Available upload bandwidth at clients far exceeds user demand.

  10. Users generally view large fraction of short videos. • But less than 20% of the users view more than 60% of videos larger than 30 minutes. User behavior - Churn

  11. User Behavior • Fraction of sessions that start at the beginning of a video and have no interactivity is important in the success of a peer-assisted VoD. • For < 30 min videos, 80% of the session does not have interactivity.

  12. Content quality revolution The demand and the bitrates for VoD increase rapidly.

  13. They believe it is likely that bitrates will increase faster than client upload B/W. Traffic Evolution 1.23 2.27 Quality Growth: 50% User Growth: 33% Traffic Growth: 78.5%

  14. Contributions of this paper • The first measurement study of an on-demand video streaming system in a large scale. • We present a simple theory for peer-assisted VoD. This theory identifies 3 basic operating modes of peer-assisted VoD system. • The surplus mode, the balanced mode, and the deficit mode. • For the single-video approach, we describe 3 natural prefetching policies for exploiting surplus peer upload capacity. • No-prefetching, water-leveling, and greedy policy. • We use the 9 months MSN trace, which was collected for a client-server deployment, to drive simulations for peer-assisted deployments. • We explore the impact of peer-assisted VoD on ISPs.

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

  16. Peer-assisted VoD • Peer-assisted VoD • Users watching the video will assist in the re-distribution of the video to other users. • There is still a server (or server farm) which stores all of the publisher’s videos. • Guarantees that users playback the video at the playback rate without any quality degradation. • The server is only active when the peers alone cannot satisfy the demand. • 2 design approaches to peer-assisted VoD • Single video: a peer only redistributes the video it is currently watching. (This paper use!!) • Multiple video:a peer can redistribute a video that it previously viewed but is currently not viewing.

  17. Modelling – Single Video • The time user remains online to see the video is T • The bitrate of the video is r • Users arrive at the system with Poisson distribution rate l • M is the number of user type, where a type m user has upload link BW wm • pm: the probability that an arrival is a type m user • System’s average upload B/W of an arriving user is m =S pm wm • Expected number of type m users is pm l T • In steady state, the average total Demand isr S pm l T = r l T • The average Supply is S pml T wm = m l T • IfSupply >Demand • Surplus mode, small server load • IfSupply<Demand • Deficit mode, VERY large server load • IfSupply≈Demand • Balanced mode, medium server load

  18. Prefetch Policy • Let every peer get more video data than demand (if possible) in surplus mode. And thus can tide over deficit mode. • Peers can potentially prefetch video from each other using the peers’ surplus bandwidth. • 3 prefetching policies • No-prefetching • Each user downloads content at the playback rate r and does not prefetch content for future needs. • At any given instant of time, the user may be downloading from multiple peers as well as from the server. • Assume that each user views the video without gaps. • Water-leveling prefetching • Peer only prefetches from peers arrive before it and have sufficient upload bandwidth, and demand is depend on the user buffer level. • Make all the peers to have the same buffer levels of prefetched content. • Greedy prefetching • Each user simply dedicates its remaining upload BW to the next user right after itself. • For each user i, donate it’s upload BW and aggregated BW to user i+1 [4] C. Huang, J. Li, and K. W. Ross, “Peer-Assisted VoD: Making Internet Video Distribution Cheap,” IPTPS, Bellevue, WA, Feb. 2007.

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

  20. Methodology • Discrete-event simulator. • Driven by 9 months of MSN Video trace. • 2 videos • Gold stream: the most popular video, popular for a few days. • Silver stream: the second most popular video, popular for a month. • Focus on the balanced mode. • Greedy prefetching. • no P2P: the resources used by the pure client-server deployment.

  21. Simulation: Non-early-departure Trace • P2P deployment at the current quality level, typically no server resources are needed. Some resources needed when few concurrent users.

  22. Simulation: Early departure (No interaction) • When video length > 30mins, 80%+ users don’t finish the whole video. Table 4: Server rates (in Mbps) under different system modes with early departures. April 2006. • Even with early departures peer-assistance can provide a good improvement. • Prefetching continues to provide improvements over non-prefetching.

  23. Simulation: Full Trace • How to deal with buffer holes? • As user may skip part of the video. • 2 strategies • Conservative: assume that user upload BW=0 after the first interaction. • Optimistic: ignore all interactions, there is no hole in the user’s buffer.

  24. Results of full trace simulation (1/2) • Due to interactivity, a user might have holes in its buffer. • The actual performance will lie between these two bounds.

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

  26. Cost Reduction With peer-assisted VoD, server BW reduction from 2.2Gbps to 79.4Mbps on Dec. 2006.

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

  28. ISP-unfriendly P2P VoD • ISPs, based on business relations, will form economic entities. • 3 relationships between ISPs: • 1) transit relationship (also called customer-provider) • one ISP purchasing Internet access from another ISP and paying for the bandwidth usage. • 2) sibling relationship • the interconnection among several ISPs belonging to the same organization. • 3) peering relationship • ISPs pairing with each other. Peering ISPs can exchange traffic directly, which would otherwise have to go through their providers. • Traffic do not pass through the boundary won’t be charged. • ISP-unfriendly P2P will cause large amount of traffic.

  29. Simulation results of unfriendly P2P Most P2P VoD crosses ISP boundaries.

  30. Simulation results of friendly P2P • Peers lies in different economic entities do not assist each other. Table 8: Server bandwidth (in Mbps) in an ISP-optimized scenario. • Silver stream single video, 5000 distinct video distributions. • Top 10 more popular videos among the 12000 in traces. • When an entity contains few peers, the sharing becomes more difficult as well, and the server bandwidth is increased accordingly.

  31. Conclusion • Peer-assisted VoD, with the proper prefetching policy, can dramatically reduce server bandwidth costs. • Peer-assisted VoD can be both server and ISP friendly.

  32. References • [3] B. Cheng, X. Liu, Z. Zhang, and H. Jin, “A Measurement Study of a Peer-to-Peer Video-on-Demand System,” IPTPS, Bellevue, WA, Feb. 2007. • [4] C. Huang, J. Li, and K. W. Ross, “Peer-Assisted VoD: Making Internet Video Distribution Cheap,” IPTPS, Bellevue, WA, Feb. 2007. • [19] A. Al Hamra, E. W. Biersack, and G. Urvoy-Keller, “A Pull-based Approach for a VoD Service in P2P Networks,” IEEE HSNMC, Toulouse, France, Jul. 2004. • [20] Y. Cui, B. Li, and K. Nahrstedt, “oStream: Asynchronous Streaming Multicast in Application-Layer Overlay Networks,” IEEE JSAC, 22(1), 2004. • [21] J. Li, Y. Cui, and B. Chang, “PeerStreaming: Design and Implementation of an On-Demand Distributed Streaming System with DRM Capabilities,” Multimedia Systems Journal, 2007. • [22] S. Annapureddy, C. Gkantsidis, P. R. Rodriguez, and L. Massoulie, “Providing Video-on-Demand Using Peer-to-Peer Networks,” Microsoft Research Technical Report, MSR-TR-2005-147, Oct. 2005.

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