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Multicast Data Dissemination

Multicast Data Dissemination. Wang Lam Special University Oral Examination 7 July 2004. Contents. Current multicast networks Contributions Data scheduling Network issues Related and future work Conclusion. Traditional data service: one-to-one Multicast networks: one-to-many

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Multicast Data Dissemination

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  1. Multicast Data Dissemination Wang Lam Special University Oral Examination 7 July 2004

  2. Contents • Current multicast networks • Contributions • Data scheduling • Network issues • Related and future work • Conclusion

  3. Traditional data service: one-to-one Multicast networks: one-to-many IP: multicast group addresses (IPv4: 224.0.0.0 - 239.255.255.255; IPv6: FF00::/8) Network bottlenecks Client joins Unreliable delivery Datagrams (UDP) Current multicast networks

  4. Client joins Unreliable delivery Datagrams Supports varying bandwidth clients All requested data must arrive Data arranged to optimize performance Multicast data dissemination

  5. Principal contributions • Data scheduling • Minimize delay for clients requesting many items • Scheduling for subscribers and downloaders 1 • Networking issues • Reliable delivery 2 • Splitting bandwidth into channels

  6. Principal contributions • Data scheduling • Scheduling for subscribers and downloaders

  7. Subscribers and downloaders • Data scheduling • Scheduling for subscribers and downloaders • Distributing data for a Web repository • Metrics and techniques • Sample results

  8. The multicast source • Stanford WebBase • 100+ million Web pages • Additional benefits of multicast WWW multicast server clients crawler repository indexing and analysis

  9. A multicast facility • Clients issue requests to server • Clients listen to shared multicast • Server schedules data onto multicast • Downloaders and subscribers Multicast server clients

  10. Clients request multiple items • Broadcast disks: one-item “response time” • Multicast: client delay is different • Subscribers: freshness and age

  11. Example scheduler: Circ • Arbitrarily order data items • Send requested data

  12. Example scheduler: Pop • Send most requested data

  13. Example scheduler: R/Q • Number of requesting clients • Smallest request size

  14. Example scheduler: R/Q • Number of requesting clients • Smallest request size

  15. Some results for subscribers • Choice of scheduler depends on performance metric • Update frequency has little effect

  16. Downloaders and subscribers

  17. Downloaders and subscribers

  18. Downloaders and subscribers

  19. Summary • Differences from broadcast disks • Downloaders and subscribers • Studied design tradeoffs for various metrics and techniques

  20. Principal contributions • Data scheduling • Minimize delay for clients requesting many items • Scheduling for subscribers and downloaders • Networking issues • Reliable delivery 2 • Splitting bandwidth into channels

  21. Principal contributions • Networking issues • Reliable delivery

  22. Principal contributions • Networking issues • Reliable delivery • Multicast server model • Reliability techniques • Sample results • Other challenges

  23. The multicast source • Stanford WebBase • 100+ million Web pages • Network loss <5% to >20% WWW multicast server clients crawler repository indexing and analysis

  24. A multicast facility • Clients issue requests to server • Clients listen to shared multicast • Server schedules data onto multicast • Data channel unreliable multicast server clients

  25. Forward Error Correction • Compute fixed fraction of redundant data • Reconstruct from subset of bits • Vary padding by item data FEC data FEC requests requests

  26. Forward Error Correction • Compute fixed fraction of redundant data • Reconstruct from subset of bits • Vary padding by item

  27. Forward Error Correction • Compute fixed fraction of redundant data • Reconstruct from subset of bits • Vary padding by item

  28. Forward Error Correction • Compute fixed fraction of redundant data • Reconstruct from subset of bits • Vary padding by item

  29. Forward Error Correction • Compute fixed fraction of redundant data • Reconstruct from subset of bits • Vary padding by item FEC(0.2R)

  30. Retransmission • Wait for NAK • Queue retransmission of enough bits • Queue only on selected NAKs data data requests requests

  31. Retransmission • Wait for NAK • Queue retransmission of enough bits • Queue only on selected NAKs

  32. Retransmission • Wait for NAK • Queue retransmission of enough bits • Queue only on selected NAKs NAK NAK

  33. Retransmission • Wait for NAK • Queue retransmission of enough bits • Queue only on selected NAKs

  34. Retransmission • Wait for NAK • Queue retransmission of enough bits • Queue only on selected NAKs

  35. Retransmission • Wait for NAK • Queue retransmission of enough bits • Queue only on selected NAKs

  36. Retransmission • Wait for NAK • Queue retransmission of enough bits • Queue only on selected NAKs

  37. Retransmission • Wait for NAK • Queue retransmission of enough bits • Queue only on selected NAKs NAK NAK R(1)

  38. Rescheduling • Do nothing • Rerequest data item • Combine with prior reliability schemes data data requests requests

  39. Rescheduling • Do nothing • Rerequest data item • Combine with prior reliability schemes NAK NAK

  40. Clients of Uniform Loss Rates

  41. Clients of Tiered Loss Rates

  42. Clients of Tiered Loss Rates

  43. Additional results • Error-correcting packets help retransmissions • Variable FEC can outperform matched-rate FEC • Data-in-progress announcement can slightly help new clients

  44. Summary • Multicast server scenario allows a variety of reliability techniques • Techniques form many combinations • Studied design tradeoffs

  45. Principal contributions • Data scheduling • Minimize delay for clients requesting many items • Scheduling for subscribers and downloaders • Networking issues • Reliable delivery • Splitting bandwidth into channels

  46. Publications • W. Lam and H. Garcia-Molina, “Multicasting a Data Repository,” WebDB 2001 • W. Lam and H. Garcia-Molina, “Multicasting a Changing Repository,” ICDE 2003 • W. Lam and H. Garcia-Molina, “Reliably Networking a Multicast Repository,” SRDS 2003 • W. Lam and H. Garcia-Molina, “Slicing Broadcast Disks,” submitted for publication • W. Lam and H. Garcia-Molina, “Implementing Multicast Data Dissemination,” technical report

  47. Publications (Stanford WebBase) • J. Cho, T. Haveliwala, W. Lam, S. Raghavan, A. Paepcke, and H. Garcia-Molina, “Stanford WebBase Components and Applications”http://www-diglib.stanford.edu/~testbed/doc2/WebBase/Web crawler and client code:ftp://db.stanford.edu/pub/digital_library/

  48. Related work • Broadcast disks • Web caching • Publish/subscribe systems • Video on demand • Reliable multicast protocols • Layered multicast protocols

  49. Next steps • Other kinds of clients • On-the-fly processing • Partially ordered clients • Opportunistic clients • Distributed servers • Request mining

  50. http://www.cs.stanford.edu/~wlam/compsci/

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