基于节点兴趣和Q-learning的P2P网络搜索机制

doi:10.11896/jsjkx.190400002

Abstract

Abstract: Adding smartphone devices to the resource sharing system based on unstructured P2P network can satisfy people’s requirements for diversity,convenience,high frequency,real-time and high efficiency of resource sharing.However,the expansion of network scale and the increase of network node heterogeneity will inevitably lead to the decrease of system resource search efficiency,the sharp increase of redundant information and the more non-network.To solve these problems,an improved resource search mechanism based on node interest and Q-learning was designed.Firstly,nodes are clustered according to interest similarity,and interest sets are divided.Then,interest trees are constructed according to the capability values of interest sets.This structure avoids the generation of message loops,which greatly reduces redundant information.In resource search,flooding algorithm is used to forward messages in interest trees,and Q-learning-based message forwarding mechanism is used among interest trees,which is constantly strengthened.The most likely paths to obtain the target resources are transformed,and query messages are propagated preferentially on these paths.In addition,for the “hot spot” resource problem,an adaptive hot spot resource index mechanism was designed to reduce the repeated path searching and redundant message volume.To solve the problem of node failure,the root node redundancy mechanism and the strategy method of piggyback detection were given.The analysis results show that the method can reduce message redundancy caused by root node failure and common node failure respectively.The simulation results show that compared with GBI-BI algorithm and Interest CN algorithm,the proposed search algorithm can improve hit rate,shorten response time,reduce redundant information,and has better comprehensive performance.Finally,it solves the problems of low efficiency of resource search and high overhead of network traffic caused by the addition of smartphone devices to P2P network.

Key words: Node failure, Node interest, Q-learning, Search algorithms, Unstructured P2P network

CLC Number:

TP393

LI Long-fei,ZHANG Jing-zhou,WANG Peng-de,GUO Peng-jun. P2P Network Search Mechanism Based on Node Interest and Q-learning[J].Computer Science, 2020, 47(2): 221-226.

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P2P Network Search Mechanism Based on Node Interest and Q-learning

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