Super-Peer网络中基于语义的分簇算法研究  

Research of Semantic-based Clustering Algorithm for Super-peer Network

作　　者：乔百友[1] 王国仁[1] 

机构地区：[1]东北大学信息科学与工程学院,辽宁沈阳110004

出　　处：《小型微型计算机系统》2008年第2期213-218,共6页Journal of Chinese Computer Systems

基　　金：国家自然科学基金项目(60573089)资助;辽宁省自然科学基金项目(20052031)资助;国家"八六三"计划项目(2006AA09Z139)资助

摘　　要：在P2P网络中,利用共享数据的语义信息,将网络划分成不同的语义簇是提高网络查找性能、增强网络可扩展性的有效手段.然而现有的基于分类层次的语义分簇方法较少考虑簇之间的负载平衡问题,这必然会影响网络的性能.为此本文提出了两种针对分类层次语义空间的自组织语义分簇算法,即语义优先分簇算法SFCA和负载平衡优先分簇算法LBFCA,这两种算法能够根据网络的负载动态的将网络划分成不同的语义簇,并很好的保持了簇中数据的语义关系和簇之间的负载平衡.实验表明这两种分簇算法具有良好的性能和可扩展性.In a P2P network, partitioning the network into distinct semantic clusters can efficiently increase the efficiency of searching and enhance scalability of the network. However, existing semantic clustering approaches based on the taxonomy hierarchy take little account of load balancing problem among clusters, which inevitably compromise network performance. To solve the problem, two semantic-based self-organized algorithms aimed at taxonomy hierarchy semantic space are proposed in this paper ：semantic first clustering algorithm （SFCA） and load balance first clustering algorithm （LBFCA）,which can dynamically partition the network into distinct semantic clusters according to network loads, with semantic relationship among data in clusters and load balance among clusters all well maintained. The experiment indicates good performance and scalability of these two clustering algorithms.

关 键 词：P2P 语义分簇 分类层次 负载平衡 

分 类 号：TP393[自动化与计算机技术—计算机应用技术]