Recently, Peer-to-Peer (P2P) has attracted a great deal of interest in industry and research literature. P2P systems are application layer networks, in which logically distinct computing elements - peers, bear comparable roles and responsibilities. P2P enables peers to share resources in a distributed manner. Existing P2P systems work well but are inefficient with respect to information retrieval. Some measurements show that more than half of the current Internet traffic is P2P traffic. Some search methods currently used by P2P groups flood the network, thus consuming a lot of bandwidth. In addition, some P2P applications require some forms of global knowledge of peer resources.
Caching is one way to improve the performance of any system that makes repetitive requests. This thesis proposes a selective query-forwarding scheme based on caching. This simple caching mechanism improves efficiency and scalability in information retrieval for P2P systems. Query processing is expedited by caching similar queries or replies, thus making searches more efficient.
The performance of this caching-based search algorithm is evaluated and compared with two existing P2P search algorithms (flooding and Random Walk) in P2P file sharing systems. The simulation experiments are designed and performed based on some measurement and empirical data. The results show this caching-based scheme is an attractive technique for keyword based searching in P2P systems. In some cases it achieves 75\% query hits through caching. Its performance is also superior in that it consumes less bandwidth and takes less time to satisfy queries. Finally, this approach doesn't incur additional network traffic to develop knowledge on resource location and thus scales well with the size of the network.
