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Designing Overlay Multicast Networks for Streaming

Designing Overlay Multicast Networks for Streaming. Jevan Saks Bruce Maggs Konstantin Andreev Adam Meyerson. Delivering Streaming Media. Server bottlenecks Points of failure Can only serve about 50Mbps of streams Network bottlenecks Unpredictable topology

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Designing Overlay Multicast Networks for Streaming

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  1. Designing Overlay Multicast Networks for Streaming Jevan Saks Bruce Maggs Konstantin Andreev Adam Meyerson

  2. Delivering Streaming Media • Server bottlenecks • Points of failure • Can only serve about 50Mbps of streams • Network bottlenecks • Unpredictable topology • Best-effort delivery = No guarantees

  3. 1 X 34 1 2 3 4 x 123 4 X X X 1 2 3 4 x 123 4 x 123 4 Live Streaming Reflectors Entry Point Encoder Edge Servers (Sinks)

  4. Proposed Solution • A less centralized approach: • Entry Point • Source of the stream, sends to reflectors • Reflector • Intermediate server, can split and retransmit • Edge Server • Reconstructs a better quality stream from what it receives and serves the end user

  5. Approach • Three-tier structure S – Sources R - Reflectors D - Sinks

  6. Considerations • Cost • Capacity • Bandwidth • Quality • Reliability

  7. IP Parameters • Success requirements () • Failure probabilities (p) • Cost on edges (c) • Cost on reflectors (r) • Fanout restrictions (F)

  8. Integer Program • Indicator variables: • zi – reflector i used • yki– stream k sent to reflector i • xkij – stream k sent to sink j through reflector i

  9. Constraints • Minimize: • Subject To:

  10. Solving the IP • ILOG Cplex (concert library) • DashOptimization Xpress-MP • GLPK (GNU Linear Programming Kit)

  11. An Example (Thanks to graphviz)

  12. Approximation • Why? IP is slow, and topology is large and changes quickly • Randomized rounding: • Cost violated by factor O(log n) • Fanout/weight constraints violated by factor of at most 2 • Modified GAP Assignment • Uses max-flow on a graph of size O(|R|¢|D|) • Cost violated again by O(log n) • Fanout/weight constraints violated by another factor of at most 2

  13. Comparison Approx Solution (Cost = 52) IP Solution (Cost = 51)

  14. In Progress • Multi-source approximations • Timing comparisons • Violations in “average-case” scenarios

  15. Extensions • Capacities on all edges • Capacities from reflector-edgeserver • Bandwidth requirements • Color constraints

  16. Extensions Reflectors Entry Point UU Net Encoder Edge Server (Sink) BMM Net

  17. Extension’s Constraints • Color constraints whereR=R1[ R2[… [ Rm (Ri is the set of reflectors on theithISP) • Here different colors represent different ISPs • There is no added value in serving to a fixed sink from two reflectors of the same color.

  18. Future Work • Use real-world data from Akamai • Implement extensions • Improve approximation?

  19. Best Case Scenario?

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