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A Dynamic SGP Selection Algorithm based on Evolutionary Game for Hybrid P2P Streaming System |
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PP: 973-981 |
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Author(s) |
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Jing Chen,
Yangjie Cao,
Li Li,
Xiaoshe Dong,
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Abstract |
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| Due to high efficiency and good scalability, hierarchical hybrid P2P architecture has drawn more and more attentions in P2P
video streaming applications recently. The problem about Super Group Peers (SGPs) selection, which is the key problem in hierarchical
hybrid P2P architecture, is becoming highly challenging because super peers must be selected from a huge and dynamically changing
network. In this paper, we propose a SGP selection game model based on evolutionary game framework and analyze its evolutionarily
stable strategies in theory. Moreover, we propose a distributed Q-Learning algorithm, which has the ability to make the peers converge
to the ESSs based on its own payoff history. Compared to the randomly super peer selection scheme in traditional P2P streaming
systems, experiment results show that the proposed algorithm achieves better performance in terms of social welfare, average upload
rates of SGPs, and keeps the upload capacity of the P2P streaming system increasing steadily with the number of peers increasing. |
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