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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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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

  • Experimental evaluation

    • Tracker

    • Segment Scheduler

  • Conclusion and future work


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


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


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


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


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


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


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


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


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


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


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


Tracker performanceSimulations

  • 350 sessions from the most popular video

  • peer arrival a Poisson process with λ=1 peers/sec


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


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


Segment Scheduler performanceJump Delay

  • Greedy policy achieves the lowest delay

  • Adaptive hybrid allocates at least some bandwidth to download rare segments


Segment scheduler performanceSeeder Load

  • Proposed adaptive hybrid best compromise between low seeder load and jump delay


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.


Future work

  • Extend Kangaroo to support adaptive video quality

  • Plans to deploy Kangaroo as a Content Distribution Network.


Questions?


More questions?


Tracker scalabilityTests


Dynamic batching


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