An Adaptive K-random Walks Method for Peer-to-Peer Networks

Abstract

Designing an intelligent search method in peer-to-peer networks will significantly affect efficiency of the network taking into account sending a search query to nodes which have more probably stored the desired object. Machine learning techniques such as learning automaton can be used as an appropriate tool for this purpose. This paper tries to present a search method based on the learning automaton for the peer-to-peer networks, in which each node is selected according to values stored in its memory for sending the search queries rather than being selected randomly. The probable values are stored in tables and they indicate history of the node in previous searches for finding the desired object. For evaluation, simulation is used to demonstrate that the proposed algorithm outperforms K-random walk method which randomly sends the search queries to the nodes.