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Adaptive Peer-to-Peer Routing with Proximity

  • Chu Yee Liau
  • Achmad Nizar Hidayanto
  • Stephane Bressan
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 2736)

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.

Keywords

Reinforcement Learning Pruning Strategy Pruning Method Reinforcement Learning Model Pruning Approach 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2003

Authors and Affiliations

  • Chu Yee Liau
    • 1
  • Achmad Nizar Hidayanto
    • 2
  • Stephane Bressan
    • 1
  1. 1.School of Computing, Department of Computer ScienceNational University of Singapore 
  2. 2.Faculty of Computer ScienceUniversity of Indonesia 

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