基于兴趣聚类的网格资源发现算法

Interest-clustering based on grid resource discovery algorithm

为了更有效地解决网格资源的搜索和定位问题,提出一种以P2P形式实现的、基于兴趣聚类的非集中式网格资源发现算法。算法采用被动学习方式,通过用户的访问历史抽取节点的兴趣属性,将节点按照兴趣属性划分为多个簇,资源发现请求在簇内朋友节点之间传播,查找失败后,将请求路由到与其兴趣最相似的其他簇内。仿真测试表明,算法稳定高效,相比传统算法在低开销情况下性能有显著的提高。

This paper proposed a grid resource discovery algorithm： peer-to-peer and decentralized interest-clustering based algorithm to address the search and location of issues. The algorithm learned passively interest attributes between nodes from history search results, divided nodes into interest-clusters. Search request was propagated between nodes with similar interest within interest-clusters. Simulation results show that, compared to the traditional algorithm, this algorithm improves query etfficiency notably without a significant increase in load.