Can Internet Video-on-Demand Be Profitable? Cheng Huang, Jin Li (Microsoft Research), Keith W. Ross...
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Transcript of Can Internet Video-on-Demand Be Profitable? Cheng Huang, Jin Li (Microsoft Research), Keith W. Ross...
Can Internet Video-on-Demand Can Internet Video-on-Demand Be Profitable? Be Profitable?
Cheng Huang, Jin Li (Microsoft Cheng Huang, Jin Li (Microsoft Research), Keith W. Ross Research), Keith W. Ross (Polytechnic University)(Polytechnic University)
ACM SIGCOMM 2007 ACM SIGCOMM 2007
OutlinesOutlines
MotivationMotivation Trace – User demand & behaviorTrace – User demand & behavior Peer assisted VoDPeer assisted VoD
– TheoryTheory– Real-trace-driven simulationReal-trace-driven simulation
Cross ISP traffic issueCross ISP traffic issue ConclusionConclusion
MotivationMotivation
Saving money for huge content Saving money for huge content providers such as MS, Youtubeproviders such as MS, Youtube
Video quality is just acceptableVideo quality is just acceptable
User demand +++
Video quality+++
Traffic+
ISP Charge+Client Server
User BW +
Video quality+
User BW +++
Video quality+++
Traffic++++++++
ISP Charge+++++++P2P
Traffic++
ISP Charge++
User BW ++++++
Video quality+++++++
Traffic+++
ISP Charge+++
P2P ArchitectureP2P Architecture
Peers will assist each other and Peers will assist each other and won’t consume the server BWwon’t consume the server BW
Each peer have contribution to the Each peer have contribution to the whole systemwhole system
Throw the ball back to the ISPsThrow the ball back to the ISPs– The traffic does not disappear, it The traffic does not disappear, it
moved to somewhere elsemoved to somewhere else
OutlinesOutlines
MotivationMotivation Trace – User demand & behaviorTrace – User demand & behavior Peer assisted VoDPeer assisted VoD
– TheoryTheory– Real-trace-driven simulationReal-trace-driven simulation
Cross ISP traffic issueCross ISP traffic issue ConclusionConclusion
Trace AnalysisTrace Analysis
Using a trace contains 590M Using a trace contains 590M requests and more than 59000 requests and more than 59000 videos from Microsoft MSN Video videos from Microsoft MSN Video (MMS)(MMS)
From April to December, 2006From April to December, 2006
Video PopularityVideo Popularity
The more skewed, the much betterThe more skewed, the much better
Download bandwidthDownload bandwidth
Use Use – ISP download/upload pricing table ISP download/upload pricing table – Downlink distribution Downlink distribution
to generate upload bw distributionto generate upload bw distribution
Demand V.S. SupportDemand V.S. Support
User behavior - ChurnUser behavior - Churn
User Behavior - User Behavior - InteractionInteraction
Content quality Content quality revolutionrevolution
Traffic EvolutionTraffic Evolution
2.271.23
Quality Growth: 50%User Growth: 33%Traffic Growth: 78.5%
OutlinesOutlines
MotivationMotivation Trace – User demand & behaviorTrace – User demand & behavior Peer assisted VoDPeer assisted VoD
– TheoryTheory– Real-trace-driven simulationReal-trace-driven simulation
Cross ISP traffic issueCross ISP traffic issue ConclusionConclusion
P2P MethodologiesP2P Methodologies
Users arrive with poison Users arrive with poison distributiondistribution
Exhaustive search for available Exhaustive search for available upload BWupload BW
100
Video rate: 6060
3040
40
0 10
100
0
0
70 Total Demand60 x 4 = 240
Total Support100+40+30+100 = 270
System statusSystem status
IfIf Support Support >> DemandDemand– Surplus mode, Surplus mode, smallsmall server load server load
IfIf SupportSupport << DemandDemand
– Deficit mode, Deficit mode, VERY largeVERY large server server loadload
IfIf SupportSupport ≈≈ DemandDemand– Balanced mode, medium server loadBalanced mode, medium server load
Prefetch PolicyPrefetch Policy
When the system status vibrates When the system status vibrates between surplus and deficit modebetween surplus and deficit mode
Let every peer get more video data Let every peer get more video data than demand (if possible) in than demand (if possible) in surplus modesurplus mode– And thus they can tide over deficit And thus they can tide over deficit
phasephase
OutlinesOutlines
MotivationMotivation Trace – User demand & behaviorTrace – User demand & behavior Peer assisted VoDPeer assisted VoD
– TheoryTheory– Real-trace-driven simulationReal-trace-driven simulation
Cross ISP traffic issueCross ISP traffic issue ConclusionConclusion
MethodologyMethodology
Event-based simulatorEvent-based simulator Driven by 9 months of MSN Video Driven by 9 months of MSN Video
tracetrace Use greedy prefetch for P2P-VoDUse greedy prefetch for P2P-VoD
– For each user i, donate it’s upload BW For each user i, donate it’s upload BW and aggregated BW to user i+1and aggregated BW to user i+1
– If user i’s buffer point is smaller than If user i’s buffer point is smaller than user i+1’suser i+1’s
BW allocate to user i+1 is no more than user BW allocate to user i+1 is no more than user ii
Trace-driven simulationTrace-driven simulationLevelLevel
Non-early-departure TraceNon-early-departure Trace Non-user-interaction TraceNon-user-interaction Trace Full TraceFull Trace
Simulation: Non-early-Simulation: Non-early-departuredeparture
Simulation: Early departure Simulation: Early departure (No interaction)(No interaction)
When video length > 30mins, 80%When video length > 30mins, 80%+ users don’t finish the whole + users don’t finish the whole videovideo
Simulation: Full Simulation: Full
How to deal with buffer holesHow to deal with buffer holes– As user may skip part of the videoAs user may skip part of the video
Two strategiesTwo strategies– Conservative: Assume that user Conservative: Assume that user
BW=0 after the first interactionBW=0 after the first interaction– Optimistic: Ignore all interactionsOptimistic: Ignore all interactions
Results of full trace Results of full trace simulation (1/2)simulation (1/2)
Results of full trace Results of full trace simulation (2/2) simulation (2/2)
OutlinesOutlines
MotivationMotivation Trace – User demand & behaviorTrace – User demand & behavior Peer assisted VoDPeer assisted VoD
– TheoryTheory– Real-trace-driven simulationReal-trace-driven simulation
Cross ISP traffic issueCross ISP traffic issue ConclusionConclusion
ISP-unfriendly P2P VoDISP-unfriendly P2P VoD
ISPs, based on business relations, ISPs, based on business relations, will form economic entitieswill form economic entities– Traffic do not pass through the Traffic do not pass through the
boundary won’t be chargedboundary won’t be charged
ISP-unfriendly P2P will cause large ISP-unfriendly P2P will cause large amount of trafficamount of traffic
Simulation results of Simulation results of unfriendly P2Punfriendly P2P
Simulation results of Simulation results of friendlyfriendly P2P P2P
Peers lies in different economic Peers lies in different economic entities do not assist each otherentities do not assist each other
Conclusion (Pros)Conclusion (Pros)
This paper gives a representative This paper gives a representative trace analysis that breaks the trace analysis that breaks the myth of upload BW problemsmyth of upload BW problems
Successfully address the Successfully address the importance of the P2P cross-ISP importance of the P2P cross-ISP problemproblem
Conclusions (Cons)Conclusions (Cons)
Weak and unrealistic P2P modelsWeak and unrealistic P2P models Unclear comparisons between Unclear comparisons between
each P2P strategies and each P2P strategies and simulationssimulations
Thank YouThank You