Search performance analysis and robust search algorithm in unstructured peer-to-peer networks

Abstract

Recently peer-to-peer networks (P2P) have gained great attention and popularity. One key challenging aspect in a P2P resource sharing environment is an efficient searching algorithm. This is especially important for Gnutella-like decentralized and unstructured networks due to the power-law degree distributions. We propose a hybrid search algorithm that decides the number of running walkers dynamically with respect to peers' topological information and search time state. It is able to control the extent of messages generating temporally by the simulated annealing mechanism, thus being a scalable search. Furthermore, we present a unified quantitative search performance metric, search efficiency, to objectively capture dynamic behavior of various search algorithms in terms of scalability, reliability and responsiveness. We quantitatively characterize, through simulations, the performance of various existing search algorithms. The proposed algorithm outperforms others in terms of search efficiency in both the local and global search spaces.