A Simple Model for Analyzing P2P Streaming Protocols

1. A Simple Model for Analyzing P2P Streaming Protocols Zhou Yipeng Chiu DahMing John, C.S. Lui The Chinese University of Hong Kong

2. Outline • Introduction • Model & Chunk Selection Strategies • Simulation • Conclusion

3. Bottleneck Waste Bandwidth Introduction • Unicast Client server is the bottleneck and waste bandwidth Router

4. Untapped bandwidth resource Weak point Introduction • Application Layer Multicast (or CDN) • Rely on a single distribution tree Leaf peers server

5. Server Peer Peer Peer Fully connected Peer Peer Peer Introduction P2P Streaming System -P2P resolves this scalability problem by using all resources of all clients. It is like using multiple trees simultaneously to deliver content. Peers maintain: * buffer * neighbor list

6. Introduction • P2P application: -file distribution, p2p streaming • Summary work on p2p streaming: -PPlive, PPstream, CoolStreaming, BiTos -Much work on system study, architecture design and measurement but little theoretic work • Our Contributions: -Analytical Models on p2p streaming system to better understand -Chunk selection strategy study and a new strategy is proposed. -Trade off between continuity and scalability

7. Outline • Introduction • Model & Chunk Selection Strategies • Simulation • Conclusion

8. t=1 1 t=2 2 1 t=3 3 2 1 Model & Chunk Selection Strategies • How buffer works? • Server sends out chunks sequentially. • Peer downloads one chunk every time slot • Buffer shits ahead one position one time slot server playback ………. Buffer

9. server 1/M playback 1 2 …………… n 1 2 …………… n 1 2 …………… n 1/M … M peers 1/M Model & Chunk Selection Strategies • M peerswith the same playback requirement • Each has a playback buffer • In each time slot, the server randomly selects one peer and uploads one chunk • Users’ metric is the continuity, defined as p(n) , the probability chunk n available • To compute p(n), recursively compute p(i). p(i) is defined as: p(i)=prob(position i filled)

10. 1 2 …………… n 1 2 …………… n P2p technology effect Model & Chunk Selection Strategies • Each peer’s buffer is a sliding window • In each time slot, each peer downloads a chunk fromserver or its neighbor • q(i) = the probability Buf[i] gets filled at this time slot, for i>1 p(1)=1/M p(n)=? time=t sliding window t+1 p(1)=1/M

11. Model & Chunk Selection Strategies • w(i) = probability peer wants to fillBuf[i] w(i)=1-p(i) • h(i) = probability the selected peer hasthe content for Buf[i] h(i)=p(i) • s(i) = Buf[i] determined by chunk selection strategy sliding window p(1)=1/M p(n) peer 1 2 …… i … n neighbor 1 2 .….. i … n p(1)=1/M

12. playback 1 2 3 4 5 6 7 8 Buffer map X X X X RF Selection Greedy Selection Model & Chunk Selection Strategies • GreedyStrategy -try to fill the empty buffer closest to playback • Rarest FirstStrategy -try to fill the empty buffer for the newest chunksince p(i) is an increasing function, this means “Rarest First” • An example

13. w(i) h(i) h(i) s(i) w(i) s(i) Model & Chunk Selection Strategies • Greedy p(i+1)=p(i)+ (1-p(i)) * p(i) * (1-p(1)-p(n)+p(i+1)) • Rarest first p(i+1)=p(i)+ (1-p(i)) * p(i) * (1-p(i)) Also studied • continuous forms for these difference equations to study sensitivity • Simulation to validate models

14. Model & Chunk Selection Strategies • From ourmodels we can get the following conclusions: • Rarest First Strategy is more scalable than the Greedy Strategy as the peer population increases. • The Greedy Strategy can achieve better continuity than Rarest First Strategy for small number of peers.

15. Buffer map 1 …….. .. m m+1 ....……… n First do RF Second do Greedy A New Chunk Selection Strategy • Partition the buffer into [1,m] and [m+1,n] • Use RF for [1,m] first • If no chunks available for download by RF, use Greedy for [m+1,n] • Difference equations become for i = 1,…,m-1 for i = m, … n-1

16. Outline • Introduction • Model & Chunk Selection Strategies • Simulation • Conclusion

17. Comparing Different Chunk Selection Strategies What do you mean by “better”? • Playback continuity: p(n) as large as possible • Start-up Latency: Given buffer size (n) and relatively large peerpopulation (M) • “Rarest first” is better in continuity! • “Greedy” is the best in start-up latency • “Mixed” is the best one of them

18. Simulation • M=1000 • N=40 • In simulation, • # neighbors=60 • Uploads at most 2 in each time slotfor one peer • Validate our model

19. Simulation Rarest First • 1000 peers, 40 buffer • Compare three strategies, especially the curve for Mixed. Mixed Greedy

20. Simulation RF Mixed Mixed RF Greedy Greedy • 1000 peers, buffer length varies from 20 to 40. • For different buffer sizes • Mixed achieves bestcontinuity than both RF and Greedy • Mixed has better start-up latency than RF

21. Simulation RF Greedy • For (a), there are 40 peers. Greedy is better. • For (b), the continuity requirement is fixed at 0.93. RF is better RF Greedy

22. Simulation Mixed • Simulate 1000 peers, 2000 time slots • Continuity is the average continuity of all peers • Continuity for Mixed is more consistent, as well highest

23. Simulation How to adapt m for the mixed strategy Mixed RF • Adjust m so that p(m) achieves a target probability (e.g. 0.3) • In simulation study, 100 new peers arrive every 100 slots • m adapts to a larger value as population increases

24. Outline • Introduction • Model & Chunk Selection Strategies • Simulation • Conclusion

25. Conclusion • Related work -Coolstreaming, BiTos • Summary work on p2p streaming: -There are many designed p2p streaming systems, such as PPLive, PPstream -Many measurement papers on these system -Little work on model analysis -Little study on chunk selection strategies • Our Contribution: -Analytical Models on p2p streaming system to better understand -Chunk selection strategy study -Mixed strategy is proposed, which is better than RF or Greedy -Trade off between continuity and scalability

26. Thank You