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Enhancing Search Performance on Gnutella-Like P2P Systems

机译：在类似Gnutella的P2P系统上提高搜索性能

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摘要

The big challenges facing the search techniques on Gnutella-like peer-to-peer networks are search efficiency and quality of search results. In this paper, leveraging information retrieval (IR) algorithms such as Vector Space Model (VSM) and relevance ranking algorithms, we present GES (Gnutella with Efficient Search) to improve search performance. The key idea is that GES uses a distributed topology adaptation algorithm to organize semantically relevant nodes into same semantic groups by using the notion of node vector. Given a query, GES employs an efficient search protocol to direct the query to the most relevant semantic groups for answers, thereby achieving high recall with probing only a small fraction of nodes. To the best of our knowledge, GES is the first to identify node vector size as an important role in impacting search performance and to show that the node vector size offers a good trade-off between search performance and bandwidth cost. Moreover, GES adopts automatic query expansion and local data clustering to improve search performance. We show that GES is efficient and even outperforms the centralized node clustering system SETS. For example, in the scenario where node capacity is heterogeneous, GES can achieve 73 percent recall when probing only 20 percent nodes, outperforming SETS by about 18 percent.
机译：类似于Gnutella的对等网络上的搜索技术面临的最大挑战是搜索效率和搜索结果的质量。在本文中，利用向量空间模型（VSM）和相关性排序算法等信息检索（IR）算法，我们提出了GES（具有高效搜索功能的Gnutella）以提高搜索性能。关键思想是GES使用分布式拓扑自适应算法，通过使用节点向量的概念将语义相关的节点组织到相同的语义组中。对于给定的查询，GES使用有效的搜索协议将查询定向到最相关的语义组以获取答案，从而仅探测一小部分节点即可实现较高的查全率。据我们所知，GES是第一个将节点矢量大小确定为影响搜索性能的重要角色，并表明节点矢量大小在搜索性能和带宽成本之间提供了很好的折衷方案。此外，GES采用自动查询扩展和本地数据聚类来提高搜索性能。我们证明GES是高效的，甚至优于集中式节点集群系统SETS。例如，在节点容量异构的情况下，当仅探测20％的节点时，GES可以实现73％的召回率，比SETS高出约18％。

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