Adaptive peer-to-peer routing with proximity

Chu Yee Liau; Achmad Nizar Hidayanto; Stephane Bressan

doi:10.1007/978-3-540-45227-0_45

Adaptive peer-to-peer routing with proximity

Chu Yee Liau, Achmad Nizar Hidayanto, Stephane Bressan

Department of Computer Science

Research output: Contribution to journal › Article › peer-review

2 Citations (Scopus)

Abstract

In this paper, we presented a routing strategy for requests in unstructured peer-to-peer networks. The strategy is based on the adaptive routing Q-routing. The strategy uses reinforcement learning to estimate the cost of routing a request. Such a strategy is scalable only if the routing indices are of reasonable size. We proposed and comparatively evaluated three methods for the pruning for the pruning of the routing indices. Our experiments confirm the validity of the adaptive routing and the scalability of a pruning approach based on a pruning strategy considering the popularity of the resources.

Original language	English
Pages (from-to)	454-463
Number of pages	10
Journal	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume	2736
DOIs	https://doi.org/10.1007/978-3-540-45227-0_45
Publication status	Published - 2003

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AB - In this paper, we presented a routing strategy for requests in unstructured peer-to-peer networks. The strategy is based on the adaptive routing Q-routing. The strategy uses reinforcement learning to estimate the cost of routing a request. Such a strategy is scalable only if the routing indices are of reasonable size. We proposed and comparatively evaluated three methods for the pruning for the pruning of the routing indices. Our experiments confirm the validity of the adaptive routing and the scalability of a pruning approach based on a pruning strategy considering the popularity of the resources.

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JO - Lecture Notes in Computer Science

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Adaptive peer-to-peer routing with proximity

Abstract

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