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On Peer-to-Peer Media Streaming

On Peer-to-Peer Media Streaming. Dongyan Xu Mohamed Heffeda Susanne Hamrusch Bharat Bhargava. 2002 International Conference on Distributed Computing Systems. Outline. Introduction P2P Media Streaming Model Optimal Media Data Assignment Fast System Capacity Amplification Simulation

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On Peer-to-Peer Media Streaming

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  1. On Peer-to-Peer Media Streaming Dongyan Xu Mohamed Heffeda Susanne Hamrusch Bharat Bhargava 2002 International Conference on Distributed Computing Systems

  2. Outline • Introduction • P2P Media Streaming Model • Optimal Media Data Assignment • Fast System Capacity Amplification • Simulation • Conclusion

  3. IntroductionCategory of P2P system • Main difference between a general P2P system and a P2P media system is the data sharing mode • Open-after-downloading mode • Play-while-downloading mode

  4. IntroductionCharacteristics of a P2P media system • Self-growingThe more peers it serves，the larger the capacity it will have • Server-lessSuch as opening a large number of simultaneous connection • HeterogeneousDifferent out-bound bandwidth contribution to the system

  5. IntroductionCharacteristics of a P2P media system • Many-to-oneMultiple supplying peers in one real-time streaming session

  6. IntroductionProblems of P2P media system • Media data assignment for a multi-supplier peer-to-peer streaming session • Fast amplification of the P2P streaming capacity

  7. P2P Media Streaming Model • Roles of peersEach supplying peer participates in at most one P2P streaming session at any time • Bandwidth of peers • R0 : denote the playback rate of the media data • Rin(Pr) = R0 • Rout(Ps) = R0/2n (R0/2 , R0/4 , …. R0/2N)

  8. P2P Media Streaming Model • Classes of peers • Classify the peers into N classes according to their out-bound bandwidth offer • Class-n peer : offer out-bound bandwidth R0/2n (1 ≦ n ≦ N ) • Capacity of the P2P streaming system • Segments of media data • Media data be partitioned into small sequential segments of equal sizes • δt of each segment is the same

  9. Optimal Media Data Assignment Bad Case Requesting peer : Pr Supplying peers : P1s , P2s , P3s , P4s ( R0/2 , R0/4 , R0/8 , R0/8) P1s : 8k+1 , 8k+2 , 8k+3 , 8k+4 P2s : 8k+4 , 8k+5 P3s : 8k+6 P4s : 8k+7 ( k = 0, 1, 2, 3, …. )

  10. Optimal Media Data Assignment Optimal Case Requesting peer : Pr Supplying peers : P1s , P2s , P3s , P4s ( R0/2 , R0/4 , R0/8 , R0/8) • The lowest class among supplying peer is class-n • Computes the assignment of the first 2n segments

  11. Optimal Media Data Assignment • The algorithm OTSp2p compute an optimal media data assignment achieves the minimum buffering delay • The minimum buffering delay

  12. Fast System Capacity Amplification Class-1 : P3s , P4s , P3r Class-2 : P1s , P2s , P1r, P2r Average waiting time (0+T+2T)/3 = T Waiting time : interval between requesting peer first streaming request and the earliest time it can be admitted T : duration of the P2P streaming session

  13. Fast System Capacity Amplification Average waiting time (T+T+0)/3 = 2T/3 • Different admission decisions lead to different growth of streaming capacity • Higher-class requesting peers will lead to a faster amplification of the system capacity

  14. Fast System Capacity AmplificationDistributed admission control protocol (DACp2p) • Key features • Supplying peer can decides whether or not to participate in a streaming session by probability • Requesting peer may send a reminder to a busy supplying peer Ps

  15. Fast System Capacity AmplificationDACp2p – Supplying Peers • Each Ps maintains an admission probability vector <Pr[1] , Pr[2], ..Pr[N]> • How to determine probability vector • Suppose Ps is class-k peerPr[i] = 1.0 when 1≦i ≦kPr[i] = 1/2i-k when k<i ≦Nclass i is favored class of Ps , if Pr[i] =1.0 • If Ps idle，then probability vector will be updated after a period of Toutk < i ≦ N, Pr[i] = Pr[i]*2

  16. Fast System Capacity AmplificationDACp2p – Supplying Peers (3) If Ps finished serving a streaming，will update its probability vector • During the streaming session，did not receive any request of its favored classk < i ≦ N, Pr[i] = Pr[i]*2 • If received one request of its favored class，request peer left a reminder to Ps，if k is the highest favored class of requesting peer which left a reminderPr[i] = 1.0 when 1≦i ≦kPr[i] = 1/2i-k when k<i ≦N

  17. Fast System Capacity AmplificationDACp2p – Requesting Peers • Pr obtains a list of M randomly supplying peers，and directly contact the candidate from high to low classes • Pr will be admitted • Pass the probabilistic admission test • Rsum = R0 • Pr will be rejected • Pr will leave a reminder to a busy Ps who currently favors the class of Pr • Backoff for at least a period of Tbkf befor making the request again

  18. Simulation • Number of requesting peers : 50000 • Number of seed supplying peers : 100 • Each seed peers is a class-1 peer • Show time of video : 60 min • Class distribution of requesting peersclass-1 : 10%class-2 : 10%class-3 : 40%class-4 : 40% • M = 8 • Tout = 20 min • Tbkf = 10 min • Simulate period : 144 hours • During the first 72 hours，the 50000 peers make their first streaming requests

  19. Simulation System capacity amplification

  20. Simulation NDACp2p DACp2p Request admission rate

  21. Simulation DACp2p NDACp2p Average buffering delay

  22. Conclusion • Algorithm OTSp2p which computes optimal media data assignments for P2P media streaming • Algorithm DACp2p which achieves fast system capacity amplification and creates an incentive for peers to offer their truly available out-bound bandwidth

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