Peer-to-peer systems have emerged as a popular way to share huge volumes of data. The usability of these systems depends on effective techniques to find and retrieve data; however, current techniques used in existing P2P systems are often very inefficient. In this paper, we present three techniques for efficient search in P2P systems. We present the design of these techniques, and then evaluate them using a combination of analysis and experiments over Gnutella, the largest open P2P system in operation. We show that while our techniques maintain the same quality of results as currently used techniques, they use up to 5 times fewer resources. In addition, we designed our techniques to be simple, so that they can be easily incorporated into existing systems for immediate impact.
Finding information in a peer-to-peer system currently requires either a costly and vulnerable central index, or flooding the network with queries. In this paper we introduce the concept of Routing Indices (RIs), which allow nodes to forward queries to neighbors that are more likely to have answers. If a node cannot answer a query, it forwards the query to a subset of its neighbors, based on its local RI, rather than by selecting neighbors at random or by flooding the network by forwarding the query to all neighbors. We present three RI schemes: the compound, the hop-count, and the exponential routing indices. We evaluate their performance via simulations, and find that RIs can improve performance by one or two orders of magnitude vs. a flooding-based system, and by up to 100% vs. a random forwarding system. We also discuss the tradeoffs between the different RI schemes and highlight the effects of key design variables on system performance.
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