Nash bargaining between friends for cooperative data distribution in a social peer-to-peer swarming system

Guilin Wang, Haojun Zhang, Yanqin Zhu, Qijin Ji, Haifeng Shen

    Research output: Contribution to conferencePaper

    Abstract

    Online social networks and peer-to-peer (P2P) data swarming are a natural match, but a significant distinction between a traditional P2P swarming system and its social version is the social ties among peers which suppress the free riding behavior and make cooperation among peers feasible. In this paper, we present a game theoretical formulation for cooperative data distribution based on friend coalitions in a social P2P swarming system and derive a Nash bargaining solution for a two-player bargaining game with the analysis of Pareto optimality and fairness. Both our analytical and experimental results show that the proposed strategies can effectively stimulate cooperation among peers and significantly improve the efficiency and fairness of data distribution compared to the traditional non-cooperative P2P swarming systems.

    Original languageEnglish
    Pages1490-1495
    Number of pages6
    DOIs
    Publication statusPublished - 1 Jan 2013
    EventThe 2013 IEEE International Conference on Machine Learning and Cybernetics -
    Duration: 14 Jul 2013 → …

    Conference

    ConferenceThe 2013 IEEE International Conference on Machine Learning and Cybernetics
    Period14/07/13 → …

    Keywords

    • cooperative data distribution
    • Nash bargaining solution
    • Online social networks
    • P2P swarming

    Cite this

    Wang, G., Zhang, H., Zhu, Y., Ji, Q., & Shen, H. (2013). Nash bargaining between friends for cooperative data distribution in a social peer-to-peer swarming system. 1490-1495. Paper presented at The 2013 IEEE International Conference on Machine Learning and Cybernetics, . https://doi.org/10.1109/ICMLC.2013.6890840