1 / 23

Routing Indices For Peer-to-Peer Systems

Routing Indices For Peer-to-Peer Systems. Arturo Crespo, Hector Garcia-Molina Stanford ICDCS 2002. Motivation and ideas. Search (text) documents with specific keyword (category) in P2P network  content-based Users only interested in TOP 20 results Each peer stores statistics of

deo
Télécharger la présentation

Routing Indices For Peer-to-Peer Systems

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript

  1. Routing Indices For Peer-to-Peer Systems Arturo Crespo, Hector Garcia-Molina StanfordICDCS 2002

  2. Motivation and ideas • Search (text) documents with specific keyword (category) in P2P network  content-based • Users only interested in TOP 20 results • Each peer stores statistics of • Documents shared by itself • Documents shared by its neighbors • Route query to a “good” peer • Sequential search vs parallel search

  3. Proposed Methods • Compound RI - naive • Hop-count RI - improved • Exponential RI - best

  4. What is routing indices? • For A, there are • 100 documents available from B (and its descendents) • 20 belong to Database category • 10 belong to Theory category • 30 belong to Languages category • “Goodness” of a neighbor

  5. Computing goodness • For documents of “databases” and “languages”

  6. D+A+J D+A+I Updating of routing indices New connection RI propagation

  7. Proposed Methods • Compound RI – naive • Hop-count RI – improved • Exponential RI - best

  8. Problems and improvements 250 items 300 items • Improved cost model • take into account of query messages generated • Less update cost • RI propagates through limited hops (horizon) • Robust against cycles

  9. Hop-count RI • For W, it can reach • 30 documents from Y 1 hop away •  Y has 30 documents • 50 documents from Y 2 hops away •  Y1,Y2 have 50 documents

  10. Goodness measure in Hop-count RI • Goodness of

  11. Proposed Methods • Compound RI – naive • Hop-count RI – improved • Exponential RI - best

  12. Improvements • Hop-count RI exhibits • High storage cost • High update cost of RI • Compress RI of different Hops together • Similar to Compound RI with RI update method differs

  13. RI Update I changed to  70, 30, 10, 20, 50

  14. RI Update 70 30 10 20 50 D update I’s row as 70, 30, 10, 20, 50

  15. RI Update 70 30 10 20 50 D sent J’s update as 590, 86.67, 130, 70, 121.67

  16. RI Update 70 30 10 20 50 590 86.67 130 70 121.67 D send A and update as 140, 75, 3.3, 75, 100

  17. Experiment

  18. Query Message generated Why CRI, HRI, ERI perform much better in the uniform distribution?

  19. Effect of index compression

  20. Effect of cycles

  21. Query message in different network topology

  22. Update cost in different network topology

  23. END

More Related