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Anhua MA, Su PAN, and Weiwei ZHOU, “Service Migration Algorithm Based on Markov Decision Process with Multiple Service Types and Multiple System Factors,” Chinese Journal of Electronics, vol. x, no. x, pp. 1–11, xxxx doi: 10.23919/cje.2022.00.128
Citation: Anhua MA, Su PAN, and Weiwei ZHOU, “Service Migration Algorithm Based on Markov Decision Process with Multiple Service Types and Multiple System Factors,” Chinese Journal of Electronics, vol. x, no. x, pp. 1–11, xxxx doi: 10.23919/cje.2022.00.128

Service Migration Algorithm Based on Markov Decision Process with Multiple Service Types and Multiple System Factors

doi: 10.23919/cje.2022.00.128
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  • Author Bio:

    Anhua MA was born in Jiangsu, China, in 1985. Master degree, Engineer, student of Broadband Wireless Communication College, Nanjing University of Posts and Telecommunications, the main research area is broadband wireless resource management. (Email: manshmily@qq.com)

    Su PAN was born in Jiangsu, China, in 1969. Doctor of Philosophy, Professor, Dean of the College of Internet of Things, Nanjing University of Posts and Telecommunications, the main research area is Broadband Wireless Resource Allocation and Mobile Internet Technology. (Email: supan@njupt.edu.cn)

    Weiwei ZHOU was born in Jiangsu, China, in 1992. She received her dual Ph.D. degree from Nanjing University of Posts and Telecommunications and University of Technology Sydney in 2022. She is now working in School of Computer Science and Technology, Nanjing Tech University. Her current interests include admission control and resource allocation in the wireless networks. (Email: zhouweiwei92@163.com)

  • Corresponding author: Email: manshmily@qq.com
  • Received Date: 2022-03-22
  • Accepted Date: 2022-11-07
  • Available Online: 2024-03-20
  • This paper proposes a Markov decision process based service migration algorithm to satisfy Quality of Service(QoS) requirements when the terminals leave the original server. Services were divided into real-time services and non-real-time services, each type of them has different requirements on transmission bandwidth and latency, which were considered in the revenue function. Different values were assigned to the weight coefficients of QoS parameters for different service types in the revenue and cost functions so as to distinguish the differences between the two service types. The overall revenue was used for migration decisions, rather than fixed threshold or instant revenue. The Markov decision process was used to maximize the overall revenue of the system. Simulation results show that the proposed algorithm obtained more revenue compared with the existing works.
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