一个邻近感知的兴趣集群P2P文件共享系统

Haiying Shen
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引用次数: 3

摘要

高效的文件查询对P2P文件共享系统的整体性能至关重要。根据共同兴趣对节点进行聚类可以显著提高文件查询的效率。另一方面,通过物理接近度对对等点进行聚类也可以提高文件查询性能。目前很少有研究能够基于同伴的兴趣和物理距离来聚类同伴。尽管结构化p2p提供了比非结构化p2p更高的文件查询效率,但由于其严格定义的拓扑结构,在结构化p2p中实现它甚至更加困难。本文介绍了一种基于结构化P2P的邻近感知和兴趣集群P2P文件共享系统。它根据兴趣和邻近程度对同伴进行分组。PAIS支持基于分层拓扑的复杂路由和集群策略。理论分析和仿真结果表明,PAIS系统显著降低了系统开销,提高了文件共享效率。
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PAIS: A Proximity-Aware Interest-Clustered P2P File Sharing System
Efficient file query is important to the overall performance of Peer-to-Peer (P2P) file sharing systems. Clustering peers by their common interests can significantly enhance the efficiency of file query. On the other hand, clustering peers by their physical proximity can also improve file query performance. Few current works are able to cluster peers based on both peer interest and physical proximity. It is even harder to realize it in structured P2Ps due to their strictly defined topologies, although they provide higher file query efficiency than unstructured P2Ps. In this paper, we introduce a proximity-aware and interest-clustered P2P file sharing system (PAIS) based on a structured P2P. It groups peers based on both interest and proximity. PAIS supports sophisticated routing and clustering strategies based on a hierarchical topology. Theoretical analysis and simulation results demonstrate that PAIS dramatically reduces the overhead and enhances efficiency in file sharing.
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