Exploiting locality for scalable information retrieval in peer-to-peer networks

An important problem in unstructured peer-to-peer (P2P) networks is the efficient content-based retrieval of documents shared by other peers. However, existing searching mechanisms are not scaling well because they are either based on the idea of flooding the network with queries or because they require some form of global knowledge. We propose the Intelligent Search Mechanism (ISM) which is an efficient, scalable yet simple mechanism for improving the information retrieval problem in P2P systems. Our mechanism is efficient since it is bounded by the number of neighbors and scalable because no global knowledge is required to be maintained. ISM consists of four components: A Profiling Structure which logs queryhit messages coming from neighbors, a Query Similarity function which calculates the similarity queries to a new query, RelevanceRank which is an online neighbor ranking function and a Search Mechanism which forwards queries to selected neighbors. We deploy and compare ISM with a number of other distributed search techniques over static and dynamic environments. Our experiments are performed with real data over Peerware, our middleware simulation infrastructure which is deployed on 75 workstations. Our results indicate that ISM outperforms its competitors and that in some cases it manages to achieve 100% recall rate while using only half of the network resources required by its competitors. Further, its performance is also superior with respect to the total query response time and our algorithm exhibits a learning behavior as nodes acquire more knowledge. Finally ISM works well in dynamic network topologies and in environments with replicated data sources.