Group-Based Search in Unstructured Peer-to-Peer Networks

Searching in unstructured peer-to-peer network with scalability and effectiveness is still a challenging problem. Recently in many applications, one-hop index replication becomes a fundamental technique for improving the search performance. However, when we extend one-hop index replication to multiple hops for locating both popular contents and rare objects with high lookup success rate, index storage cost and index data transfer overload become serious obstacles. In this paper, we propose a novel technique to exploit the advantage of multi-hop index replication and significantly reduce the cost of index storage. Our approach is to separate the peers and contents into groups and associating the nodes with shared files in group space. Each node works as a delegate node to storing the same group file indices which come from its neighbors. Search process is restricted in a group of peers according to the query's hash value. We study the search performance and index storage cost of random walk with group-based index replication (GBIR) through theoretical model and give the mathematical relationship between GBIR and random walk with normal index replication (IR). We further evaluate the GBIR technique by simulator-based experiments, and the results show that GBIR can achieve the similar lookup success rate to IR and reduce the index storage cost to only I/g of ER's (g is the number of groups in the system). Finally, we significantly improve the GBIR's search performance using GBIR with the highest degree neighbor routing (GBIR+).