Abstract
Due to high efficiency and good scalability, hierarchical hybrid P2P architecture has drawn more and more attention in P2P streaming research and application fields recently. The problem about super peer selection, which is the key problem in hybrid heterogeneous P2P architecture, is becoming highly challenging because super peers must be selected from a huge and dynamically changing network. A distributed super peer selection (SPS) algorithm for hybrid heterogeneous P2P streaming system based on evolutionary game is proposed in this paper. The super peer selection procedure is modeled based on evolutionary game framework firstly, and its evolutionarily stable strategies are analyzed. Then a distributedQ-learning algorithm (ESS-SPS) according to the mixed strategies by analysis is proposed for the peers to converge to the ESSs based on its own payoff history. Compared to the traditional randomly super peer selection scheme, experiments results show that the proposed ESS-SPS algorithm achieves better performance in terms of social welfare and average upload rate of super peers and keeps the upload capacity of the P2P streaming system increasing steadily with the number of peers increasing.
Highlights
Future media internet will need to be able to distribute high quality video contents in an efficient, supple, and personalized way through dynamic and heterogeneous network environments
The hierarchical hybrid topology model, which divides the peers in P2P streaming systems into super peers and ordinary peers according to the performance differences of nodes, has become the focus of recent researches especially for heterogeneous P2P streaming applications
We consider the traditional randomly super peers selection method, which is denoted as Random super peer selection (SPS) for performance comparison
Summary
Future media internet will need to be able to distribute high quality video contents in an efficient, supple, and personalized way through dynamic and heterogeneous network environments. The hierarchical hybrid topology model, which divides the peers in P2P streaming systems into super peers and ordinary peers according to the performance differences of nodes, has become the focus of recent researches especially for heterogeneous P2P streaming applications. Being different from the above works, in this study, we present a super peer selection scheme based on evolutionary game in a hybrid heterogeneous P2P streaming networking scenario. We present a distributed method for super peer selection dynamically in hybrid heterogeneous P2P streaming system. First we use evolutionary game theory framework to model the super peer selection procedure in hybrid heterogeneous P2P streaming system, and we design a distributed super peer selection algorithm (ESS-SPS) based on Q-learning according to the Evolutionarily Stable Strategies (ESSs).
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