Can internet video on demand be profitable
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Can Internet Video-on-Demand Be Profitable? PowerPoint PPT Presentation


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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?

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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

Outlines

  • Motivation

  • Trace – User demand & behavior

  • Peer assisted VoD

    • Theory

    • Real-trace-driven simulation

  • Cross ISP traffic issue

  • Conclusion


Motivation

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

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


Outlines1

Outlines

  • Motivation

  • Trace – User demand & behavior

  • Peer assisted VoD

    • Theory

    • Real-trace-driven simulation

  • Cross ISP traffic issue

  • Conclusion


Trace analysis

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

Video Popularity

  • The more skewed, the much better


Download bandwidth

Download bandwidth

  • Use

    • ISP download/upload pricing table

    • Downlink distribution

      to generate upload bw distribution


Demand v s support

Demand V.S. Support


User behavior churn

User behavior - Churn


User behavior interaction

User Behavior - Interaction


Content quality revolution

Content quality revolution


Traffic evolution

Traffic Evolution

1.23

2.27

Quality Growth: 50%

User Growth: 33%

Traffic Growth: 78.5%


Outlines2

Outlines

  • Motivation

  • Trace – User demand & behavior

  • Peer assisted VoD

    • Theory

    • Real-trace-driven simulation

  • Cross ISP traffic issue

  • Conclusion


P2p methodologies

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

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

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


Outlines3

Outlines

  • Motivation

  • Trace – User demand & behavior

  • Peer assisted VoD

    • Theory

    • Real-trace-driven simulation

  • Cross ISP traffic issue

  • Conclusion


Methodology

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 simulation level

Trace-driven simulationLevel

  • Non-early-departure Trace

  • Non-user-interaction Trace

  • Full Trace


Simulation non early departure

Simulation: Non-early-departure


Simulation early departure no interaction

Simulation: Early departure (No interaction)

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


Simulation full

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


Results of full trace simulation 1 2

Results of full trace simulation (1/2)


Results of full trace simulation 2 2

Results of full trace simulation (2/2)


Outlines4

Outlines

  • Motivation

  • Trace – User demand & behavior

  • Peer assisted VoD

    • Theory

    • Real-trace-driven simulation

  • Cross ISP traffic issue

  • Conclusion


Isp unfriendly p2p vod

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 unfriendly p2p

Simulation results of unfriendly P2P


Simulation results of friendly p2p

Simulation results of friendly P2P

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


Conclusion pros

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

Conclusions (Cons)

  • Weak and unrealistic P2P models

  • Unclear comparisons between each P2P strategies and simulations


Thank you

Thank You


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