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http://dx.doi.org/10.3837/tiis.2019.10.011

MOPSO-based Data Scheduling Scheme for P2P Streaming Systems  

Liu, Pingshan (Business School, Guilin University of Electronic Technology)
Fan, Yaqing (Guangxi Key Laboratory of Trusted Software, Guilin University of Electronic Technology)
Xiong, Xiaoyi (Guangxi Key Laboratory of Trusted Software, Guilin University of Electronic Technology)
Wen, Yimin (Guangxi Key Laboratory of Trusted Software, Guilin University of Electronic Technology)
Lu, Dianjie (School of Information Science and Engineering, Shandong Normal University)
Publication Information
KSII Transactions on Internet and Information Systems (TIIS) / v.13, no.10, 2019 , pp. 5013-5034 More about this Journal
Abstract
In the Peer-to-Peer (P2P) streaming systems, peers randomly form a network overlay to share video resources with a data scheduling scheme. A data scheduling scheme can have a great impact on system performance, which should achieve two optimal objectives at the same time ideally. The two optimization objectives are to improve the perceived video quality and maximize the network throughput, respectively. Maximizing network throughput means improving the utilization of peer's upload bandwidth. However, maximizing network throughput will result in a reduction in the perceived video quality, and vice versa. Therefore, to achieve the above two objects simultaneously, we proposed a new data scheduling scheme based on multi-objective particle swarm optimization data scheduling scheme, called MOPSO-DS scheme. To design the MOPSO-DS scheme, we first formulated the data scheduling optimization problem as a multi-objective optimization problem. Then, a multi-objective particle swarm optimization algorithm is proposed by encoding the neighbors of peers as the position vector of the particles. Through extensive simulations, we demonstrated the MOPSO-DS scheme could improve the system performance effectively.
Keywords
Peer-to-Peer; video streaming; data scheduling; multi-objective optimization; multi-objective particle swarm optimization;
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