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Fast Searching in Peer-to-Peer Networks

Fast Searching in Peer-to-Peer Networks. Self-Organizing Parallel Search Clusters Rocky Dunlap. Agenda. Peer-to-peer Networks Search Links/Index Links Model Parallel Search Clusters Self-Organizing Parallel Search Clusters Further Research. Peer-to-Peer Networks. Peer = Client + Server

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Fast Searching in Peer-to-Peer Networks

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  1. Fast Searching in Peer-to-Peer Networks Self-Organizing Parallel Search Clusters Rocky Dunlap

  2. Agenda • Peer-to-peer Networks • Search Links/Index Links Model • Parallel Search Clusters • Self-Organizing Parallel Search Clusters • Further Research

  3. Peer-to-Peer Networks • Peer = Client + Server • Anyone can send/process messages • Highly Distributed • Highly Parallel • Data-centric routing

  4. Unstructured “Loose” network structure Requires less control of peers (casual searching) Fault tolerance, churn Keyword searching Structured Specific network structure Distributed Hash Tables Smart routing Guarantees: Bounded hops Bounded state Ability to search entire network P2P Networks – Two Types

  5. Unstructured Searching ?

  6. The Problems • Query saturation – every node processes every query • Query processing redundancy • Slow response time from distant nodes • In reality, cannot search entire network (TTL) • Need a model for studying P2P networks

  7. Search Links (forwarding) SIL Model • Index Links (non-forwarding)

  8. SIL Model ?

  9. SIL Model ?

  10. SIL Model ?

  11. SIL Model ?

  12. SIL Model ?

  13. SIL Model ?

  14. SIL Model ?

  15. SIL Model ?

  16. SIL Model ?

  17. SIL Model ?

  18. SIL Model ?

  19. SIL Model ?

  20. Searches remain inside cluster Index links provide full coverage Parallel Search Clusters

  21. Parallel Search Clusters • Assumptions • Keep network essentially unstructured (keyword searching, fault tolerance) • Search rate is high • Update rate is low • Limit the number of nodes that processes query • Provide full (or high) coverage of network • Index links allow some nodes to proxy searches for others

  22. The Challenge • Self-Organizing Parallel Search Clusters • Decentralized • Nodes only know a few neighbors • Dealing with “churn” • Minimal interruption of normal operations

  23. Proposed Solution • Existing clusters split into two new clusters • Advantages • Solves origin problem (start with one cluster) • Clusters split autonomously • Automatic load balancing • Three phase approach • Color • Replicate Links • Split

  24. Splitting Cluster ! Phase 1 Coloring

  25. Splitting Cluster ! Phase 1 Coloring Color (radius = 2)

  26. Splitting Cluster Phase 1 Coloring Color (radius = 2)

  27. green red red red Splitting Cluster green red Phase 2 Replicate Links

  28. red red red Splitting Cluster red Phase 2 Replicate Links

  29. Splitting Cluster X Phase 3 Split

  30. Splitting Cluster X Phase 3 Split

  31. Splitting Cluster Phase 3 Split

  32. X X X X X X X X Splitting Cluster Phase 3 Split

  33. Splitting Cluster Phase 3 Split

  34. Splitting Cluster Phase 3 Split

  35. Further Research • Initiating the split • Choosing the radius for coloring phase • Want two clusters of same size • Overloading index links • Dealing with “churn” • Nice nodes • Not-so-nice nodes • Merge operation? • Simulation

  36. Bibliography • B. F. Cooper and H. Garcia-Molina. SIL: Modeling and Measuring Scalable Peer-to-peer Search Networks. http://www-db.stanford.edu/~cooperb/pubs/searchnets.pdf, 2003. • B. Yang and H. Garcia-Molina. Improving Search in Peer-to-Peer Networks. http://dbpubs.stanford.edu:8090/pub/2002-28, 2002.

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