• DocumentCode
    3122958
  • Title

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

  • Author

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

  • Author_Institution
    Sch. of Comput. Sci. & Technol., Soochow Univ., Suzhou, China
  • Volume
    04
  • fYear
    2013
  • fDate
    14-17 July 2013
  • Firstpage
    1490
  • Lastpage
    1495
  • 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.
  • Keywords
    Pareto optimisation; game theory; peer-to-peer computing; social networking (online); Nash bargaining solution; OSNs; P2P data swarming system; Pareto optimality; cooperative data distribution; game theoretical formulation; online social networks; social peer-to-peer swarming system; Abstracts; Analytical models; Bandwidth; Games; Indexes; NIST; Simulation; Nash bargaining solution; Online social networks; P2P swarming; cooperative data distribution;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Machine Learning and Cybernetics (ICMLC), 2013 International Conference on
  • Conference_Location
    Tianjin
  • Type

    conf

  • DOI
    10.1109/ICMLC.2013.6890840
  • Filename
    6890840