摘要
资源搜索是P2P网络基本功能及核心问题,关系到P2P网络可用性及扩展能力.尽管已提出许多无结构化P2P搜索方法,但复杂组织方式、较高搜索代价及过多维护影响其可用性.提出一个全分布无结构化P2P网络搜索方法BNS.该方法从节点自身兴趣特性出发,利用节点上资源之间语义相关,应用贝叶斯网络建立推理模型,根据相关资源历史信息进行推理,采用概率方法,将搜索导向与目标相关的节点,提高搜索性能.实验表明,该方法能够有效地提高搜索性能,消耗较少带宽且维护简单,对P2P动态变化特性具有良好适应能力.
Object discovery is a fundamental service and a critical issue in peer-to-peer （P2P） networks, which has a great impact on the performance and scalability of P2P networks. Many search methods have been proposed for unstructured peer-to-peer networks during the past few years, but complicated organization, high search cost and maintenance overhead make them less practicable. To avoid these weaknesses, the authors propose an adaptive and efficient method for search in unstructured P2P networks, the Bayesian network-based search method （BNS）, which retains the simplicity, robustness and fully decentralized nature like Gnutella. This approach assumes that if a peer has a particular resource and this resource has some relation to a resource that one is interested in, it is very likely that this peer has the resource that one is interested in as well. This scheme utilizes feedback from previous searches, including not only the feedback of an interested object itself but also those objects which have some semantic relations to the interested object. It applies Bayesian network to establish an inference model, infers probabilities based on this model, and probabilistically directs future searches. Experimental results show that the BNS method is effective, achieving high success rate and more discovered objects, low bandwidth consumption and less maintenance, and a good adaptation to changing network topologies.
出处
《计算机研究与发展》
EI
CSCD
北大核心
2009年第6期889-897,共9页
Journal of Computer Research and Development
基金
国家"八六三"高技术研究发展计划基金项目(2007AA01Z422)~~
