A game theoretic approach to provide incentive and service differentiation in P2P networks

  • Authors:
  • Richard T. B. Ma;Sam C. M. Lee;John C. S. Lui;David K. Y. Yau

  • Affiliations:
  • Chinese University of Hong Kong;Chinese University of Hong Kong;Chinese University of Hong Kong;Purdue University, West Lafayette, IN

  • Venue:
  • Proceedings of the joint international conference on Measurement and modeling of computer systems
  • Year:
  • 2004

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Abstract

Traditional peer-to-peer (P2P) networks do not provide service differentiation and incentive for users. Consequently, users can obtain services without themselves contributing any information or service to a P2P community. This leads to the "free-riding" and "tragedy of the commons" problems, in which the majority of information requests are directed towards a small number of P2P nodes willing to share their resources. The objective of this work is to enable service differentiation in a P2P network based on the amount of services each node has provided to its community, thereby encouraging all network nodes to share resources. We first introduce a resource distribution mechanism between all information sharing nodes. The mechanism is driven by a distributed algorithm which has linear time complexity and guarantees Pareto-optimal resource allocation. Besides giving incentive, the mechanism distributes resources in a way that increases the aggregate utility of the whole network. Second, we model the whole resource request and distribution process as a competition game between the competing nodes. We show that this game has a Nash equilibrium and is collusion-proof. To realize the game, we propose a protocol in which all competing nodes interact with the information providing node to reach Nash equilibrium in a dynamic and efficient manner. Experimental results are reported to illustrate that the protocol achieves its service differentiation objective and can induce productive information sharing by rational network nodes. Finally, we show that our protocol can properly adapt to different node arrival and departure events, and to different forms of network congestion.