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RSB-Topo: A Topology Adaptation Algorithm for Unstructured P2P Networks

机译:RSB-Topo:非结构化P2P网络的拓扑适应算法

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Peer-to-Peer (P2P) technology has been widely applied in today''s Internet. Unstructured P2P systems, where peers connect with each other to form dynamic, flexible and scalable networks, are commonly used for resource sharing. Resource location in unstructured P2P systems tends to use "blind search" strategies (i.e. flooding and random walk) for its simplicity and low maintenance cost. However, due to lack of location information, the performance of "blind search" is heavily relied on the network topology. Topology adaptation, by properly adjusting the topology of the P2P overlay network, is a promising approach to improve the search performance. In this paper, we use Relative Search Betweenness (RSB) to estimate nodes'' search ability. A RSB-based topology adaptation algorithm (RSB-Topo) is proposed, where peers spontaneously adjust their connections to achieve better performance. Simulation results show that our algorithm could greatly increase search success rate and search coverage, and also decrease response delay to improve the search performance in unstructured P2P networks.
机译:对等网络(P2P)技术已经广泛应用于今天的互联网。非结构化P2P系统,在同行相互连接,形成动态的,灵活的,可扩展的网络,通常用于资源共享。在非结构化P2P系统资源定位倾向于使用它的简单和低维护成本“盲目搜索”战略(即洪水和随机游走)。然而,由于缺乏位置信息,“盲目搜索”的性能主要依赖于网络的拓扑结构。拓扑调整,通过适当调整P2P覆盖网络的拓扑结构,是提高搜索性能有前途的方法。在本文中,我们使用相对搜索及中间(RSB)来估计节点'的搜索能力。一个RSB-基于拓扑自适应算法(RSB-TOPO)提出,在同龄人自发地调整自己的连接,以实现更好的性能。仿真结果表明,该算法可以大大提高搜索的成功率和搜索范围,并降低响应延迟,提高非结构化P2P网络中的搜索性能。

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