Making Search Efficient on Gnutella-Like P2P Systems

Abstract

Leveraging the state-of-the-art information retrieval (IR) algorithms like VSM and relevance ranking algorithm, we present GES, an efficient IR system built on top of Gnutella-like P2P networks. The key idea is that GES employs a distributed, content-based, and capacity-aware topology adaptation algorithm to organize nodes (each of which is represented by a node vector) into semantic groups. The intuition behind this design is that semantically associated nodes within a semantic group tend to be relevant to the same queries. Given a query, GES uses a capacity-aware search protocol based on semantic groups and selective one-hop node vector replication, to direct the query to the most relevant nodes which are responsible for the query, thereby achieving high recall with probing only a small faction of nodes. Moreover, GES adopts automatic query expansion techniques to improve quality of search results, and it is the first work to show that node vector size plays a very important role in system performance. The experimental results show that GES is very efficient, and even outperforms the centralized node clustering system like SETS.