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