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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? 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
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 +++++++
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
Outlines • Motivation • Trace – User demand & behavior • Peer assisted VoD • Theory • Real-trace-driven simulation • Cross ISP traffic issue • Conclusion
Trace Analysis • Using a trace contains 590M requests and more than 59000 videos from Microsoft MSN Video (MMS) • From April to December, 2006
Video Popularity • The more skewed, the much better
Download bandwidth • Use • ISP download/upload pricing table • Downlink distribution to generate upload bw distribution
Traffic Evolution 1.23 2.27 Quality Growth: 50% User Growth: 33% Traffic Growth: 78.5%
Outlines • Motivation • Trace – User demand & behavior • Peer assisted VoD • Theory • Real-trace-driven simulation • Cross ISP traffic issue • Conclusion
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
System status • IfSupport >Demand • Surplus mode, small server load • IfSupport<Demand • Deficit mode, VERY large server load • IfSupport≈Demand • Balanced mode, medium server load
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
Outlines • Motivation • Trace – User demand & behavior • Peer assisted VoD • Theory • Real-trace-driven simulation • Cross ISP traffic issue • Conclusion
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
Trace-driven simulationLevel • Non-early-departure Trace • Non-user-interaction Trace • Full Trace
Simulation: Early departure (No interaction) • When video length > 30mins, 80%+ users don’t finish the whole video
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
Outlines • Motivation • Trace – User demand & behavior • Peer assisted VoD • Theory • Real-trace-driven simulation • Cross ISP traffic issue • Conclusion
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
Simulation results of friendly P2P • Peers lies in different economic entities do not assist each other
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
Conclusions (Cons) • Weak and unrealistic P2P models • Unclear comparisons between each P2P strategies and simulations