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Issue No. 12 - Dec. (2013 vol. 24)
ISSN: 1045-9219
pp: 2439-2450
Haiying Shen , Clemson University, Clemson
Yuhua Lin , Clemson University, Clemson
Ze Li , Clemson University, Clemson
Peer-to-peer networks (P2Ps) use reputation systems to provide incentives for nodes to offer high quality of service (QoS) and thwart the intentions of dishonest or selfish nodes. Existing reputation systems have two problems. First, they directly regard node reputation as trust. Rather, reputation represents the opinions formed by others about a node's QoS behavior, while trust represents a node's honesty and willingness to cooperate. In addition to trust, factors such as node capacity and lifetime also influence reputation. Due to these factors' heterogeneity and variance over time, reputation cannot directly reflect a node's trust or current QoS. Second, existing reputation systems guide a node to select the server with the highest reputation, which may not actually select the highest QoS server and would overload the highest reputed nodes. This work aims to accurately reflect node trust and provide guidance for high-QoS server selection. Through experimental study, we find that node trust, available capacity, and lifetime positively affect node reputation. Based on this observation, we first propose a manual trust model and an automatic trust model that remove the influence of additional factors on reputation to truly reflect node trust. We then propose a high-QoS server selection algorithm that separately considers node trust, current available capacity, and lifetime. Extensive simulation results demonstrate the effectiveness of the trust models in accurate node trust reflection compared with an existing reputation system. Moreover, the server selection algorithm dramatically increases the success rate of service requests and avoids overloading nodes.
Servers, Peer to peer computing, Quality of service, Neural networks, TV
Haiying Shen, Yuhua Lin, Ze Li, "Refining Reputation to Truly Select High-QoS Servers in Peer-to-Peer Networks", IEEE Transactions on Parallel & Distributed Systems, vol. 24, no. , pp. 2439-2450, Dec. 2013, doi:10.1109/TPDS.2012.338
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