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Kangaroo: Video Seeking in P2P Systems

Kangaroo: Video Seeking in P2P Systems. Xiaoyuan Yang † , Minas Gjoka ¶ , Parminder Chhabra † , Athina Markopoulou ¶ , Pablo Rodriguez †. ¶ University of California, Irvine. † Telefonica Research. Outline. Motivation and contributions Related work Architecture Peers Tracker

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Kangaroo: Video Seeking in P2P Systems

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  1. Kangaroo: Video Seeking in P2P Systems Xiaoyuan Yang †, Minas Gjoka ¶, Parminder Chhabra †, Athina Markopoulou ¶, Pablo Rodriguez † ¶ University of California, Irvine † Telefonica Research

  2. Outline • Motivation and contributions • Related work • Architecture • Peers • Tracker • Experimental evaluation • Tracker • Segment Scheduler • Conclusion and future work

  3. Peer-to-peerChallenges • File sharing • reduces the service provider’s cost • allows scalability • Live Streaming • reduces upload cost, particularly important for • high quality videos, popular events • challenge: time sensitivity • Video on Demand • challenge: lack of synchronization between peers • Video on Demand with jump operations • enhances user experience • additional challenge: constant neighborhood re-adjustments

  4. Problem statement • Build a hybrid Video on Demand P2P system that • supports jumps • provides low buffering times • high swarming throughput • without overly provisioned peers and without aggressive prefetching

  5. Video on Demand P2P Related Work • Prior Work Bulletmedia - N. Vratonjic, P. Gupta, N. Knezevic, D. Kostic, A. Rowstron, “Enabling DVD-like features in P2P Video-on-Demand Systems”, in ACM P2P-TV Workshop 2007. Gridcast - B. Cheng, X. Liu, Z. Zhang, H. Jin, “A Measurement Study of a Peer-to-Peer Video-on-Demand System ”, in IPTPS 2007. PPLive – Y. Huang, T.Z. J. Fu, D.M Chiu, K.C.S. Lui, C. Huang, “Challenges, Design and Analysis of a Large-scale P2P VoD System”, in Sigcomm 2008

  6. ArchitectureKey Design Choices • Mesh-based P2P system with a pull model. • small segment size (64KB) • small active set of neighbors • Peers • adaptive hybrid segment scheduler • neighborhood manager • Smarttracker • smart neighbor selection • history based neighbor selection

  7. Architecture – Peers (1)Segment Scheduler • Segment scheduler decides what segment to download next • greedy strategy chooses segments for sequential playback • altruistic strategy chooses local-rarest segments • We propose a dynamic hybrid segment scheduler • starts with 80% greedy and 20% altruistic • ratio of sequential vs local-rarest segments varies dynamically depending on buffer size and segment deadline

  8. Architecture – Peers (2)Neighborhood manager + Peer Selection • Neighborhood manager • maintains a “healthy neighborhood’ • limits the number of active connection. • requests segments from “least useful” neighbors • Dynamic batching of “Have” messages • important reduction of control traffic

  9. Architecture – Tracker (1) • Observations: • The need of peers is guided by playback point • Peers play sequentially between jumps • Smart tracker meshes together peers that have content to exchange • Smart Neighbor Selection (SNS) returns list of peers at the same playback point • History Neighbor Selection (HNS) returns list of peers that contain needed segments

  10. Architecture – Tracker (2)Smart Neighborhood Selection (SNS) p=0 p=4 p=7 p=9 Peer A joins t=2, p=0 p=0 p=4 p=9 Peer B joins t=4, p=0 Play Point p 4 3 2 1 0 -1 -2 -3 -4 t=2 9 t=4 A B 7 4 3 2 1 0 -1 -2 -3 -4 4 t=6 A A B 1 4 3 2 1 0 -1 -2 -3 -4 4 6 8 2 Wall time t -2 A t=8 B -4 B

  11. Architecture – Tracker (3)History Neighborhood Selection (HNS) p=0 p=4,t=6 p=7,t=6 p=9,t=8 Peer A joins t=2 p=0 p=4,t=8 p=9,t=8 Peer B joins t=4 Wall time t=4 10 0 1 2 3 4 5 6 7 8 9 A A A B

  12. Architecture – Tracker (4)History Neighborhood Selection (HNS) p=0 p=4,t=6 p=7,t=6 p=9,t=8 Peer A joins t=2 p=0 p=4,t=8 p=9,t=8 Peer B joins t=4 Wall time t=7 10 0 1 2 3 4 5 6 7 8 9 A A A A A A B B B B

  13. Experimental evaluation • Experiment setup • peers rate limited at 1.5Mbps • playback rate of 1Mbps. • peer neighborhood size: 10-15 • 5 active download/upload connections • network emulated with a Modelnet cluster of 10 machines connected in a local Gigabit LAN • user behavior emulated from real traces collected from a live commercial IPTV service • Performance metrics • jump delay • seeder upload bandwidth

  14. Tracker performanceSimulations • 350 sessions from the most popular video • peer arrival a Poisson process with λ=1 peers/sec

  15. Tracker performanceSystem experiments • Comparisons with Random Neighbor Selection: • 3.5% reduction of segments uploaded at the seeder with SNS • 22% reduction of segments uploaded at the seeder with SNS+HNS

  16. Tracker Scalability • Tracker scalability depends on number of requests received by tracker: • every jump operation • triggered by neighborhood health evaluation • After experimental tests • we observed responses < 0.1ms for user behavior of 16,000 “dumb” peers • evaluated the trade-off between tracker response time and good topology connectivity

  17. Segment Scheduler performanceJump Delay • Greedy policy achieves the lowest delay • Adaptive hybrid allocates at least some bandwidth to download rare segments

  18. Segment scheduler performanceSeeder Load • Proposed adaptive hybrid best compromise between low seeder load and jump delay

  19. Conclusion • Designed a VoD system that provides good user experience without over-provisioning • Key mechanisms for this simple design • a smart scalable tracker • an adaptive hybrid segment scheduler • “least useful” peer selection • System tested with real users during 2008 Olympic games.

  20. Future work • Extend Kangaroo to support adaptive video quality • Plans to deploy Kangaroo as a Content Distribution Network.

  21. Questions?

  22. More questions?

  23. Tracker scalabilityTests

  24. Dynamic batching

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