Scalable Grouping Based on Neuro-Fuzzy Clustering for P2P Networks
KES-AMSTA '09 Proceedings of the Third KES International Symposium on Agent and Multi-Agent Systems: Technologies and Applications
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Improving search performance is an important issue in peer-to-peer (P2P) network systems. Although Distributed Hash Tables (DHTs) route queries more efficiently than flooding does, it's complicated for DHTs to support keyword-based searches. Emerging large-scale P2P systems employ clustering to reduce message overheads and provide system scalability. In this paper, we propose an architecture based on interest groups to improve search performance in P2P networks. Query messages are first sent to interested peers that have high probability to hit the queries. Simulation results show that the proposed architecture outperforms related works in terms of message overheads, search latency, and query hit ratio.