MIKAD:一种多兴趣聚类的P2P网络模型

Multi-interest clustering P2P network model

为降低P2P网络中数据检索的路由跳数,提高路由效率,提出一种多兴趣聚类的P2P网络模型MIKAD(multi-interest clustering KAD)。该模型通过文档聚类算法维护节点兴趣,将结构化网络Kademlia与兴趣聚类相结合,使兴趣相似节点在逻辑上位于邻居位置,提高了P2P网络中路由的效率。同时利用关键词的同义词特性,降低了网络的复杂度,提高了检索的精度。最后使用PeerSim模拟器对模型进行了实验测试,结果表明,随着节点及数据增多,该模型具有较好的查询效率。

To reduce the routing hops of data retrieval in P2P network,proposed the MIKAD（multi-interest clustering KAD） network model.In the model,nodes＇ interest maintained by the documents cluster algorithm,through combining Kademlia and interesting-cluster together,and putting the nodes of similar interest in the neighboring position,which could improve routing efficiency in the peer-to-peer network.Simultaneously used synonymous characteristic of Keywords to lower the network complexity and improve retrieval accuracy.Simulation experimental results show that this model can achieve better search performance with the increase of nodes and data.