Efficient search in unstructured peer-to-peer networks

The huge popularity of recent peer-to-peer (P2P) file sharing systems has been mainly driven by the scalability of their architectures and the flexibility of their search facilities. Such systems are usually designed as unstructured P2P networks, because they impose few constraints on topology and data placement and support highly versatile search mechanisms. A major limitation of unstructured P2P networks lies, however, in the inefficiency of their search algorithms, which are usually based on simple flooding schemes. In this paper, we propose novel mechanisms for improving search efficiency in unstructured P2P networks. Unlike other approaches, we do not rely on specialized search algorithms; instead, the peers perform local dynamic topology adaptations, based on the query traffic patterns, in order to spontaneously create communities of peers that share similar interests. The basic premise of such semantic communities is that file requests have a high probability of being fulfilled within the community they originate from, therefore increasing the search efficiency. We propose further extensions to balance the load among the peers and reduce the query traffic. Extensive simulations under realistic operating conditions substantiate that our techniques significantly improve the search efficiency and reduce the network load.