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]

In order to provide scalable live streaming services, we propose an Interoverlay Optimization scheme, IOO. Instead of selecting better paths in the same overlay, IOO constructs efficient paths using peers in different overlays so as to 1) improve global resource utilization of peer-to-peer (P2P) streaming networks, 2) assign resources based on their locality and delay, 3) guarantee streaming service quality by using the nearest peers, even when such peers might belong to different overlays, and 4) balance the load among the group (streaming overlay) members. We compare the performance of IOO with existing approaches through trace-driven simulations. Results show that IOO outperforms previous schemes in terms of resource utilization and the quality of service (QoS) of streaming services. The IOO scheme has been implemented in an Internet-based live streaming system, called AnySee. AnySee was successfully released in the summer of 2004 in the China Educational Research Network (CERNET). More than 60,000 users enjoy massive entertainment programs, including TV programs, movies, and academic conferences videos. [ABSTRACT FROM AUTHOR]

Efficient and effective full-text retrieval in unstructured peer-to-peer networks remains a challenge in the research community. First, it is difficult, if not impossible, for unstructured P2P systems to effectively locate items with guaranteed recall. Second, existing schemes to improve search success rate often rely on replicating a large number of item replicas across the wide area network, incurring a large amount of communication and storage costs. In this paper, we propose BloomCast, an efficient and effective full-text retrieval scheme, in unstructured P2P networks. By leveraging a hybrid P2P protocol, BloomCast replicates the items uniformly at random across the P2P networks, achieving a guaranteed recall at a communication cost of O(\sqrtN), where N is the size of the network. Furthermore, by casting Bloom Filters instead of the raw documents across the network, BloomCast significantly reduces the communication and storage costs for replication. We demonstrate the power of BloomCast design through both mathematical proof and comprehensive simulations based on the query logs from a major commercial search engine and NIST TREC WT10G data collection. Results show that BloomCast achieves an average query recall of 91 percent, which outperforms the existing WP algorithm by 18 percent, while BloomCast greatly reduces the search latency for query processing by 57 percent. [ABSTRACT FROM PUBLISHER]