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

• Published in: 2013 International Conference on Machine Learning and Cybernetics Vol. 4; pp. 1490 - 1495
• Main Authors: Guilin Wang, Haojun Zhang, Yanqin Zhu, Qijin Ji, Haifeng Shen
• Format: Conference Proceeding
• Language: English
• Published: IEEE 01-07-2013
• Subjects: Abstracts; Analytical models; Bandwidth; cooperative data distribution; Games; Indexes; Nash bargaining solution; NIST; Online social networks; P2P swarming; Simulation
• ISSN: 2160-133X
• DOI: 10.1109/ICMLC.2013.6890840

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.