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Exploiting Content Localities for Efficient Search in P2P Systems

Exploiting Content Localities for Efficient Search in P2P Systems. Lei Guo 1 Song Jiang 2 Li Xiao 3 and Xiaodong Zhang 1 1 College of William and Mary, USA 2 Los Alamos National Laboratory, USA 3 Michigan State University, USA. Network manager. Don’t be so greedy, the Internet

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Exploiting Content Localities for Efficient Search in P2P Systems

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  1. Exploiting Content Localities for Efficient Search in P2P Systems Lei Guo1 Song Jiang2 Li Xiao3 and Xiaodong Zhang1 1College of William and Mary, USA 2Los Alamos National Laboratory, USA 3Michigan State University, USA

  2. Network manager Don’t be so greedy, the Internet is shared by all the people! Peer-to-Peer Search • Two Performance Objectives • Individual peer: improve the search quality • Internet management: minimize the search cost Fast, fast, fast, and the more the better! P2P user

  3. Existing Solutions • Generally aim to one of the two objectives and have performance limits to the other • Flooding: • Most effective for user’s experience • Least efficient for network resource utilization • Random walk: • Traffic efficient, but • Long response time and limited number of search results

  4. Super-Node Architecture • Super-node • Index server for its leaf nodes • Problems • Index based search has limits • Hard for full-text search • Impossible for encrypted content search • Not responsible for the content quality of its leaf nodes • The structure becomes large and inefficient. • A leaf node has to connect to multiple super-nodes to avoid single point failure • Generating an increasingly large number of super-nodes

  5. Gnutella Population in One Day (2003) number of peers number of super peers One super node only connects to 3-4 peers in average!

  6. Outline • Our Measurement Study • CAC: Constructing Content Abundant Cluster • SPIRP: Selectively Prefetching Indices from Responding Peers • CAC-SPIRP: Combining CAC and SPIRP • Performance Evaluation • Conclusion

  7. Our Measurement Study • Existing measurement studies • A small percentage of popular files account for most shared storage and transmissions in P2P systems • A small amount of peers contribute majority number of files in P2P. • They are only the indirect evidence of content locality • Some files may be never accessed, or accessed rarely • Our purpose • Fully understand the localities in the peer community and individual peers • Get first-hand traces for our simulation study

  8. Trace Collection • Four-day crawling on the Gnutella network • Open source code of LimeWire Gnutella • Session based collection (for the whole life time of peers) • Query sending traces by different peers • 25,764 peers • 409,129 queries • Content indices of different peers • Full indices of 18,255 peers • 37% free riders

  9. Queries Replied by Top Query Responders (%) Results Replied by Top Result Providers (%) 100 100 100 100 Percentage of Peers (%) 80 80 60 60 80 80 40 40 20 20 60 60 0 0 100 101 102 103 104 Number of Queries 40 40 Percentage of Peers (%) 20 20 0 0 0 20 40 60 80 100 Top Content Providers (in percentage) 100 102 104 106 Number of Results Content Locality in the Peer Community A small group of peers can reply nearly all queries and provide most of results

  10. 100 60 50 80 40 60 30 40 20 20 10 0 0 The Localities of Search Interests of Individual Peers • A peer can get search results from a small number of its top query responders: they share the same search interests • Similar to the idea in Locality of Interest scheme, but our conclusion is based on real P2P systems Result Contributions (%) Query Contributions (%) top 1 top 10 top 5% top 10% top 20% top 1 top 10 top 5% top 10% top 20% Top Query Responders Top Result Providers

  11. Reorganizing the P2P Management Structure • Clustering those small number of content abundant peers • Prefetching indices from those top query responders

  12. CAC: Constructing Content Abundant Cluster • Objectives • Clustering those small number of content abundant peers in P2P overlay • Providing high quality and fast service • Content Abundant Cluster • An overlay on top of P2P network • Self-evaluate, self-identify, and self-organize • Persistent public service for all peers in the system • Strong content-based (not index-based)

  13. 2 3 2 1 2 1 2 1 1 3 0 0 2 0 0 2 1 3 0 0 0 2 3 3 1 1 1 2 2 3 2 2 2 3 2 3 CAC: System Structure Clustering Leveling Dynamic Update C A C X 4

  14. CAC: Search Operations • Queries are sent to CAC first • Up-flowing operation • Flooding in CAC • Unsatisfied queries are propagated from CAC to the whole system • Down-flooding operation • Propagated from low levels to high levels

  15. 2 3 2 1 2 1 2 1 1 3 0 0 2 0 0 2 1 3 0 0 0 3 1 1 1 2 2 2 2 2 3 2 3 Up-flowing C A C 4

  16. 2 3 2 1 2 1 2 1 1 3 0 0 2 0 0 2 1 3 0 0 0 3 1 1 1 2 2 2 2 2 3 2 3 Down-flooding Unused links C A C 4

  17. SPIRP: Selectively Prefetching Indices from Responding Peers • Basic operations • Peer I initiates a query q • Query hits: displays the results • Misses: sends q • Peer R responds query q • sends query results as well as • piggybacks indices of all shared files • Peer I receives response • Display the searching results as well as • stores piggybacked indices • Indices updating • Active updating indices by responding peers • Updating indices demanded by requesting peers • Replacement of file indices

  18. ♫ Where are these files? SPIRP Technique Classic music R1 I Pop music R2 Query = “Beethoven mp3”

  19. Where are these files? SPIRP Technique classic R1 I pop NULL R2 Query = “Beetle mp3”

  20. SPIRP Technique classic R1 I pop R2 Query = “Beetle mp3”

  21. SPIRP Technique classic R1 No enough space to save indices I pop R2 Query = “Beetle mp3”

  22. ♫ SPIRP Technique classic R1 Replace complete I pop R2 Query = “Beetle mp3”

  23. CAC-SPIRP • CAC: application level infrastructure • Significantly reducing bandwidth consumption • Good response time when queries success in CAC • Long response time when queries fail in CAC • SPIRP: client-oriented and overlay independent • Significantly reducing response time • Small traffic when queries can be satisfied in cache • Same traffic as flooding when cache misses • CAC-SPIRP • Easy to combine the two techniques • Consider the trade-off between the two performance objectives • Has both merits of search quality and search cost

  24. Simulation Environment • Content trace and query trace • 4 day Gnutella crawling in our measurement • Overlay topology • Traces by Clip2 Distributed Search Solutions • Session duration • Pareto distribution fitted from measurement results P(x) = 14.5311 * x -1.8598

  25. Evaluation Metrics • Query success rate • CAC: success rate in CAC (normalized to flooding) • SPIRP: success rate in local cache (normalized to flooding) • Overall network traffic • accumulated communication traffics for all queries, responses, and index transferring (normalized to flooding) • Average response time • use the number of routing hops (normalized to flooding) • Evaluate for different query satisfactions • 1, 10, 50 results, representing different user demands

  26. 1 1 0.8 0.8 0.6 0.6 0.4 0.2 0.4 0 0 10 20 30 40 50 0 10 20 30 40 50 0 10 20 30 40 50 Cluster Size (In Percentage of P2P Network Size) Cluster Size (In Percentage of P2P Network Size) Cluster Size (In Percentage of P2P Network Size) 0.2 2 1.5 0 0 10 20 30 40 50 1 Cluster Size (In Percentage of P2P Network Size) 0.5 0 Performance Evaluation for CAC Overall Traffic (Normalized) Success Rate in CAC (normalized) Minimum Results = 1 Minimum Results = 10 Minimum Results = 50 Minimum Results = 1 Minimum Results = 10 Minimum Results = 50 Avg Response Time (Normalized) 5% top content abundant peers are good enough for cluster construction Minimum Results = 1 Minimum Results = 10 Minimum Results = 50

  27. 1 1 2 0.8 0.8 1.5 0.6 1 0.6 0.4 0.5 0.2 0.4 0 0 0.2 0 CAC Member Selection Avg Response Time (Normalized) Success Rate in CAC (normalized) Minimum Results = 1 Minimum Results = 10 Minimum Results = 50 0 0.01 0.02 0.03 0.04 Success Response Rate of CAC Peers Minimum Results = 1 Minimum Results = 10 Minimum Results = 50 Overall Traffic (Normalized) Minimum Results = 1 Minimum Results = 10 Minimum Results = 50 0 0.01 0.02 0.03 0.04 Success Response Rate of Content-Abundant Peers • Overall traffic is not sensitive to CAC member quality • Traffic can be significantly reduced even for • randomly selected CAC members • CAC down flooding is very efficient 0 0.01 0.02 0.03 0.04 Success response rate of CAC Peers

  28. Peers having 1 to 5 queries satisfied Peers having 10 to 20 queries satisfied Peers having 30 to 40 queries satisfied Peers having at least 50 queries satisfied Peers having 1 to 5 queries satisfied Peers having 10 to 20 queries satisfied Peers having 30 to 40 queries satisfied Peers having at least 50 queries satisfied 0 2 4 6 8 10 Size of Incoming Index Set Buffer (in M Bytes) Query Satisfaction = 1 Query Satisfaction = 10 Query Satisfaction = 50 0 2 4 6 8 10 Size of Incoming Index Set Buffer (in M Bytes) CAC-SPIRP Overall Performance 1 Success Rate in Local Cache 2 Average Response Time (Normalized) 0.8 1.6 0.6 0.4 1.2 0.2 0.8 0 0.4 1 Overall Traffic (Normalized) 0.8 0 0 2 4 6 8 10 0.6 Size of Incoming Index Set Buffer (in M Bytes) 0.4 CAC-SPIRP reduces both the overall traffic and response time significantly 0.2 0

  29. Conclusion • CAC-SPIRP fundamentally addresses the P2P search problem by a re-organization. • Exploiting organizational content locality • CAC: a content abundant cluster provides high quality and fast services. • Exploiting user content locality • SPIRP: a client prefetching technique to speed up search by avoiding unnecessary queries

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