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
Link To Document :
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