1. Resilient Peer-to-Peer Streaming Paper by: Venkata N. Padmanabhan Helen J. Wang Philip A. Chou Discussion Leader: Manfred Georg Presented by: Christoph Jechlitschek

2. The Problem Distribution of live streaming media to a potentially large and highly dynamic population of hosts

3. Motivation Flash crowds  Often due an event of widespread interest …  … but not always (e.g. Webcast of a birthday party)  Can effect relatively obscure sites (e.g. www.cricket.org)www.cricket.org Site becomes unreachable precisely when it is popular! Streaming server can quickly be overwhelmed  Network bandwidth is the bottleneck

4. Other solutions Content distribution networks In-house server farms Increase bandwidth Peer-to-Peer networks IP multicast

5. Why other solutions do not work Not cost effective To expensive Do not scale Force user to dedicate bandwidth Can not handle high churn rate Not widely supported

6. CoopNet Cooperative Networking Clients help server to distribute content In return the overall quality of the content increases Placing only minimum demands on peers

7. Challenges Unreliable peers  can disconnect/crash without notice Constrained and asymmetric bandwidth  Last hop is often bottleneck  Median broadband bandwidth 900 Kbps/212 Kbps  Congestion due to competing applications Reluctant users  Some ISPs charge based on usage

8. Design Decisions P2P for scalability Peers forward data only if tuned in No more upload than download Redundancy in network paths Redundancy in data

9. Redundancy in network paths A single distribution tree is vulnerable to node failure Create multiple distribution trees Split data and send it with or without redundancy over multiple trees

10. Example – Before…

11. Example – After…

12. Tree Construction Nodes inform the server when they join  Also send delay coordinates Server constructs and repairs the tree Nodes report losses to server  Aggregate reports to avoid overloading server

13. Design Decisions Short trees  Minimizes chance of disruption Tree diversity vs. efficiency  Diversity: minimizes chance of disruption  Efficiency: matches underlying network topology Quick join and leave  Number of round-trips should be small Scalability  Preferable an algorithm that uses O(1) round-trips

14. Tree Construction algorithms Randomized  Search tree top down for a node with enough capacity and appoint it as parent for new node  Alternatively search also 1 or 2 levels below and make better decision Deterministic  Each node is fertile in only one tree and sterile in all other trees  Allows shorter and more diverse trees

15. Random vs. Deterministic

16. Repairing a tree If a peer leaves it stops forwarding packets to its children Peers do not need to notify the root The root has to find and replace those nodes A 1 second repair interval gives good results

17. Redundancy in data Multiple Description Coding  Old idea, dates back to the 1970 Voice splitting work at Bell Labs  No ordering of the descriptions  Any subset of descriptions is decodable  The more descriptions received the better the image quality  Increases video stream size

18. Multiple Description Coding Order bits by their importance Split bits in the range [R i-1, R i ) into i blocks Send 1 block from each range If no more blocks to send then send error correction instead

19. Scalable Client Feedback Individual feedback from each client can overwhelm server Instead each peer reports to its parent The parent combines that report with its own and passes it to its parent periodically Not all trees have to carry feedback Feedback is used to increase or decrease redundancy in data

20. Evaluation Simulate client joins and leaves based on a 911 flash crowd trace Needed to substitute original video clip Root bandwidth20 Mbps Peer bandwidth160 Kbps Stream bandwidth160 Kbps Packet size1250 bytes GOF duration1 second # descriptions16 # trees1, 2, 4, 8, 16 Reporting interval1 second Repair interval1, 5, 10 seconds

21. Flash Crowd Trace

22. Multiple Trees

24. Randomized vs. Deterministic Tree Construction

25. MDC versus FEC

26. Conclusions P2P streaming is attractive because it is self scaling Resilience is provided by multiple distribution trees and MDC Experiments show promising results

27. Questions?

28. References http://www.research.microsoft.com/project s/CoopNet/papers/icnp2003.pdf http://www.research.microsoft.com/project s/CoopNet/papers/icnp2003.pdf Figures were taken from http://www.research.microsoft.com/~padm anab/talks/resilientP2Pstreaming- mar03.pdf http://www.research.microsoft.com/~padm anab/talks/resilientP2Pstreaming- mar03.pdf