Some statistical characteristic will emerge in unstructured P2P networks because of its large scale. Using above phenomena, an unstructured P2P overlay called Multi-Level Local Overlay, or ML2O is presented in this paper. The mathematical control on the links can generate a topology with locality in multiple scale. Theoretical analyses show that the diameter of network and the average degree of peers are all O(logn) where n is the size of peers. Based on the multi-level locality, a recursive indexing mechanism is devised in this paper, in which the index for larger locality is build from the indexes of smaller localities Finally, an unstructured P2P search algorithm called Local Pervasion and Directed Search, or LPDS is presented in this paper. LPDS will collect information from local scope by executing local pervasion. Moreover, a part of the index tree can be achieved from the collected information. LPDS can find the next hop approaching the destination in the partial index tree. Theoretical analyses show that the expectation of average search hops and communication loads produced by LPDS are all O(logn). Experimental results illustrate the scalability of LPDS on ML2O is close to structured P2P search and its robustness is close to unstructured P2P search.%无结构P2P网络拓扑随着规模的增大会出现一定的统计特性,充分应用该现象提出了一种多级局部覆盖网络(multi-level local overlay,简称ML2O)的无结构P2P覆盖网,对ML2O中节点间的连接进行恰当的数学控制后,就能使产生的拓扑具有从微观到宏观的多个粒度上的局部性.理论分析表明,ML2O的网络直径和节点平均度都是网络规模n的对数,为其上建立可扩展的无结构P2P搜索奠定了基础.给出了应用ML2O多粒度局部特性的索引机制:首先以局部为单位建立信息索引；然后在局部索引的基础上建立更大粒度局部的索引,从而形成一棵索引树；最后提出了一种局部渗透定向搜索算法(local pervasion and directed search,简称LPDS).LPDS用局部渗透收集到的信息建立部分索引树,并在树上找到更接近搜索目标的下一跳.理论分析表明,LPDS搜索算法的平均搜索跳数和通信负载都是O(logn).模拟实验结果表明,ML2O上LPDS的可扩展性接近结构化P2P搜索,其鲁棒性接近无结构P2P搜索.
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