Search

Abstract: In a hybrid peer-to-peer (P2P) system, flooding and DHT are both employed for content locating. The decision to use flooding or DHT largely depends on the population of desired data. Previous works either use local information only, or do not consider dynamic factors of P2P systems. In this paper, we propose a Popularity Adaptive Search method for Hybrid (PASH) protocol. By dynamically estimating the content popularity, PASH properly selects search methods so as to efficiently saves query traffic cost and response time. We evaluate PASH through synthetic and trace-driven simulations. The results show that PASH outperforms existing approaches and it also scales well. [Copyright &y& Elsevier]

By combining an unstructured protocol with a DHT-based global index, hybrid peer-to-peer (P2P) improves search efficiency in terms of query recall and response time. The major challenge in hybrid search is how to estimate the number of peers that can answer a given query. Existing approaches assume that such a number can be directly obtained by computing item popularity. In this work, we show that such an assumption is not always valid, and previous designs cannot distinguish whether items related to a query are distributed in many peers or are in a few peers. To address this issue, we propose QRank, a difficulty-aware hybrid search, which ranks queries by weighting keywords based on term frequency. Using rank values, QRank selects proper search strategies for queries. We conduct comprehensive trace-driven simulations to evaluate this design. Results show that QRank significantly improves the search quality as well as reducing system traffic cost compared with existing approaches. [ABSTRACT FROM AUTHOR]