Robust incentives via multi-level Tit-for-Tat: Research Articles

Authors:
Qiao Lian;Yu Peng;Mao Yang;Zheng Zhang;Yafei Dai;Xiaoming Li
Affiliations:
Roxbeam Media Network Corporation, Beijing, People's Republic of China;Peking University, Beijing, People's Republic of China;Microsoft Research Asia, Beijing, People's Republic of China;Microsoft Research Asia, Beijing, People's Republic of China;Peking University, Beijing, People's Republic of China;Peking University, Beijing, People's Republic of China
Venue:
Concurrency and Computation: Practice & Experience - Recent Advances in Peer-to-Peer Systems and Security (P2P 2006)
Year:
2008

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Abstract

Much work has been done to address the need for incentive models in real deployed peer-to-peer networks. In this paper, we discuss problems found with the incentive model in a large, deployed peer-to-peer network, Maze. We evaluate several alternatives, and propose an incentive system that generates preferences for well-behaved nodes while correctly punishing colluders. We discuss our proposal as a hybrid between Tit-for-Tat and EigenTrust, and show its effectiveness through simulation of real traces of the Maze system. Copyright © 2007 John Wiley & Sons, Ltd.