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• W2198956730 abstract "Self-reorganization offers the promise of improved performance, scalability and resilience in Peer-to-Peer (P2P) overlays. In practice however, the benefit of reorganization is often lower than the cost incurred in probing and adapting the overlay. We employ machine learning feature selection in a novel manner: to reduce communication cost thereby providing the basis of an efficient neighbor selection scheme for P2P overlays. In addition, our method enables nodes to locate and attach to peers that are likely to answer future queries with no a priori knowledge of the queries.We evaluate our neighbor classifier against live data from the Gnutella unstructured P2P network. We find Support Vector Machines with forward fitting predict suitable neighbors for future queries with over 90% accuracy while requiring minimal (<2% of the features) knowledge of the peer's files or type. By providing a means to effectively and efficiently select neighbors in a self-reorganizing overlay, this work serves as a step forward in bringing such architectures to real-world fruition." @default.
• W2198956730 created "2016-06-24" @default.
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• W2198956730 date "2007-04-10" @default.
• W2198956730 modified "2023-09-28" @default.
• W2198956730 title "Machine learning for efficient neighbor selection in unstructured P2P networks" @default.
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