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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 l.jpg

Can Internet Video-on-Demand Be Profitable?

Cheng Huang, Jin Li (Microsoft Research), Keith W. Ross (Polytechnic University)

ACM SIGCOMM 2007


Outlines l.jpg
Outlines

  • Motivation

  • Trace – User demand & behavior

  • Peer assisted VoD

    • Theory

    • Real-trace-driven simulation

  • Cross ISP traffic issue

  • Conclusion


Motivation l.jpg
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 l.jpg
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


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Outlines

  • Motivation

  • Trace – User demand & behavior

  • Peer assisted VoD

    • Theory

    • Real-trace-driven simulation

  • Cross ISP traffic issue

  • Conclusion


Trace analysis l.jpg
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 l.jpg
Video Popularity

  • The more skewed, the much better


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Download bandwidth

  • Use

    • ISP download/upload pricing table

    • Downlink distribution

      to generate upload bw distribution






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Traffic Evolution

1.23

2.27

Quality Growth: 50%

User Growth: 33%

Traffic Growth: 78.5%


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Outlines

  • Motivation

  • Trace – User demand & behavior

  • Peer assisted VoD

    • Theory

    • Real-trace-driven simulation

  • Cross ISP traffic issue

  • Conclusion


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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 l.jpg
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 l.jpg
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


Outlines18 l.jpg
Outlines

  • Motivation

  • Trace – User demand & behavior

  • Peer assisted VoD

    • Theory

    • Real-trace-driven simulation

  • Cross ISP traffic issue

  • Conclusion


Methodology l.jpg
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


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Trace-driven simulationLevel

  • Non-early-departure Trace

  • Non-user-interaction Trace

  • Full Trace



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Simulation: Early departure (No interaction)

  • When video length > 30mins, 80%+ users don’t finish the whole video


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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




Outlines26 l.jpg
Outlines

  • Motivation

  • Trace – User demand & behavior

  • Peer assisted VoD

    • Theory

    • Real-trace-driven simulation

  • Cross ISP traffic issue

  • Conclusion


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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



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Simulation results of friendly P2P

  • Peers lies in different economic entities do not assist each other


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Good for the paper

  • Large scale on-demand video streaming system measurement

  • Simulation to show peer assistance can dramatically reduce server bandwidth cost

  • Pointing out and try to solve impact of peer-assisted VoD on the cross-traffic ISPs

  • A model to explain simple operation mode of peer-assisted VoD

  • Comparison of three natural pre-fetching policies: non pre-fetching, water-level and greedy


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Bad for the paper

  • Too simple conclusion for the user upload bandwidth breakdown

  • Simple model for peer assisted VoD

  • ISP friendly Peer-assisted VoD is most likely impossible to study and apply…

  • Only study peer-assisted VoD based on pure VoD System

  • Not so many impressive results from measurements


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What we can …

  • NAT problem might solve by locality information

  • Any other models can explain more factors about VoD system or VoD system with peer assist

  • User interaction and peer churn in the Grid2.0 system are two interesting topic to study

  • QoS of peer and server cost inside peer assisted VoD are some direction for research



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