Exploiting Content Localities for Efficient Search in P2P Systems

Exploiting Content Localities for Efficient Search in P2P Systems (2004)

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by Lei Guo , Song Jiang , Li Xiao , Xiaodong Zhang

Venue:	IN: PROCEEDINGS OF THE 18TH INTERNATIONAL SYMPOSIUM ON DISTRIBUTED COMPUTING, (DISC 2004
Citations:	4 - 2 self

BibTeX

@INPROCEEDINGS{Guo04exploitingcontent,
    author = {Lei Guo and Song Jiang and Li Xiao and Xiaodong Zhang},
    title = {Exploiting Content Localities for Efficient Search in P2P Systems},
    booktitle = {IN: PROCEEDINGS OF THE 18TH INTERNATIONAL SYMPOSIUM ON DISTRIBUTED COMPUTING, (DISC 2004},
    year = {2004},
    pages = {729--742},
    publisher = {}
}

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Abstract

Existing P2P search algorithms generally target either the performance objective of improving search quality from a client's perspective, or the objective of reducing search cost from an Internet management perspective. We believe that the essential issue to be considered for designing and optimizing search algorithms in unstructured P2P networks is the trade-o# between the two performance objectives. Motivated by our observations, the locality of content serving in the peer community and the localities of search interests of individual peers, we propose CAC-SPIRP, a fast and low cost P2P searching algorithm. Our algorithm consists of two components. The first component aims to reduce the search cost by constructing a CAC (Content Abundant Cluster), where content-abundant peers self-identify, and self-organize themselves into an inter-connected cluster providing a pool of popular objects to be frequently accessed by the peer community. A query will be first routed to the CAC, and most likely to be satisfied there, significantly reducing the amount of network tra#c and the search scope. The second component in our algorithm is client oriented and aims to improve the quality of P2P search, called SPIRP (Selectively Prefetching Indices from Responding Peers). A client individually identifies a small group of peers who have the same interests as itself to prefetch their entire file indices of the related interests, minimizing unnecessary outgoing queries and significantly reducing query response time. Building SPIRP on the CAC Internet infrastructure, our algorithm combines both merits of the two components and balances the trade-o# between the two performance objectives. Our trace-driven simulations show that CAC-SPIRP significantly improves the ...

Keyphrases

content locality p2p system efficient search performance objective peer community search cost algorithm combine low cost p2p essential issue search algorithm search scope inter-connected cluster cac internet infrastructure first component aim algorithm consists p2p search content-abundant peer internet management perspective trace-driven simulation query response time p2p search algorithm search quality building spirp related interest small group second component popular object network tra entire file index individual peer unstructured p2p network content abundant cluster search interest