改进的基于概率的非结构化对等网搜索算法

Enhanced Probabilistic-based Search for Unstructured Peer-to-Peer Networks

资源发现是P2P应用所面临的最核心问题之一,无结构P2P主要采用了查询消息泛洪和信息索引机制,会造成严重的网络带宽负担以及巨大的索引维护开销。提出了一种无结构P2P环境下一种改进的概率自适应的、动态缓存和索引算法EAPS,EAPS算法依据用户结点和用户结点的邻居节点命中查询的历史信息赋予结点相应权值作为查询消息路由的依据,引导查询快速接近目标资源,同时能够解决由搭便车结点所带来的网络带宽消耗。自适应缓存机制和索引机制的引入使搜索性能大为加强。最后的实验表明,附带自适应缓存和索引的EAPS,以其高搜索成功率、很短的时间响应,能够显著地提高资源发现性能。

One of the essential problems in P2P is the strategy for resource discovery.Related methods in unstructured P2P either depend on the flooding and its variations or utilize various indices,which results in too much overhead to forward messages or too expensive cost to maintain the indices.An Enhanced adaptive,bandwidth-efficient and easily maintained search algorithm for unstructured P2P systems,EAPS,is presented.The scheme utilizes the feedback from previous searches and its neighbors to probabilistically guide future ones and solve bandwidth consumption inducing by free riders.In addition,an effective caching andindexing mechanism is introduced,which remarkably enforces the search performance.The final simulation experiment shows that the strategy can remarkably improve the search efficiency with the small average path length,high success rates,and the eminent adaptability to the change of hot resources.