On optimizing overlay topologies for search in unstructured peer-to-peer networks

Hung-Chang Hsiao, Hong Wei Su

研究成果: Article

21 引文 斯高帕斯(Scopus)

摘要

Unstructured peer-to-peer (P2P) file-sharing networks are popular in the mass market. As the peers participating in unstructured networks interconnect randomly, they rely on flooding query messages to discover objects of interest and thus introduce remarkable network traffic. Empirical measurement studies indicate that the peers in P2P networks have similar preferences, and have recently proposed unstructured P2P networks that organize participating peers by exploiting their similarity. The resultant networks may not perform searches efficiently and effectively because existing overlay topology construction algorithms often create unstructured P2P networks without performance guarantees. Thus, we propose a novel overlay formation algorithm for unstructured P2P networks. Based on the file sharing pattern exhibiting the power-law property, our proposal is unique in that it poses rigorous performance guarantees. Theoretical performance results conclude that in a constant probability, 1) searching an object in our proposed network efficiently takes O(\ln^{c} {\cal N}) hops (where c is a small constant), and 2) the search progressively and effectively exploits the similarity of peers. In addition, the success ratio of discovering an object approximates 100 percent. We validate our theoretical analysis and compare our proposal to competing algorithms in simulations. Based on the simulation results, our proposal clearly outperforms the competing algorithms in terms of 1) the hop count of routing a query message, 2) the successful ratio of resolving a query, 3) the number of messages required for resolving a query, and 4) the message overhead for maintaining and formatting the overlay.

原文English
文章編號6035690
頁(從 - 到)924-935
頁數12
期刊IEEE Transactions on Parallel and Distributed Systems
23
發行號5
DOIs
出版狀態Published - 2012 二月 13

All Science Journal Classification (ASJC) codes

  • Signal Processing
  • Hardware and Architecture
  • Computational Theory and Mathematics

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