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An Incentive Compatible Mechanism for Replica Placement in Peer-Assisted Content Distribution

An Incentive Compatible Mechanism for Replica Placement in Peer-Assisted Content Distribution
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Author(s): Prabir Bhattacharya (Concordia University, Montreal, Canada) and Minzhe Guo (University of Cincinnati, USA)
Copyright: 2020
Volume: 12
Issue: 1
Pages: 21
Source title: International Journal of Software Science and Computational Intelligence (IJSSCI)
Editor(s)-in-Chief: Brij Gupta (National Institute of Technology, Kurukshetra, India) and Andrew W.H. Ip (University of Saskatchewan, Canada)
DOI: 10.4018/IJSSCI.2020010104

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Abstract

Content delivery is a key technology on the Internet to achieve large scale, low-latency, reliable, and intelligent data delivery. Replica placement (RP) is a key machinery in content delivery systems to achieve efficient and effective content delivery. This work proposes a novel decentralized algorithm for the replica placement in peer-assisted content delivery networks with simultaneous considerations for peer incentives. By applying techniques from the algorithmic mechanism design theory, the authors show the incentive compatibility of the proposed algorithm. Experiments were conducted to validate the properties of the proposed method and comparisons were made with the state-of-the-art RP algorithms.

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