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WU Yuqin, SHEN Congqi, CHEN Shuhan, WU Chunming, LI Shunbin, Wei Ruan. Intelligent Orchestrating of IoT Microservices Based on Reinforcement Learning[J]. Chinese Journal of Electronics. doi: 10.1049/cje.2020.00.417
Citation: WU Yuqin, SHEN Congqi, CHEN Shuhan, WU Chunming, LI Shunbin, Wei Ruan. Intelligent Orchestrating of IoT Microservices Based on Reinforcement Learning[J]. Chinese Journal of Electronics. doi: 10.1049/cje.2020.00.417

Intelligent Orchestrating of IoT Microservices Based on Reinforcement Learning

doi: 10.1049/cje.2020.00.417
Funds:  The paper is supported by National Key Research and Development Project(2018YFB2100404), Fujian Natural Science Foundation (2020J01431), Ningde Normal University Innovation Team Program(2018T04), the Fundamental Research Funds for the Central Universities (Zhejiang University NGICS Platform: K20200002), and the Major Scientific Project of Zhejiang Lab (2018FD0ZX01)
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  • Author Bio:

    received the BS degree in computer science and technology from the Department of Computer Science and Technology, Xi’an University, Xi’an, China, in 2004. Her research interests include computer network communication and network security, machine learning theory, and IoT. (Email: wuyu-qin@163.com)

    received the PhD degree and is a Professor in the Department of Computer Science and Technology, Zhejiang University, China. His research interests include the nextgeneration network, network security, and network virtualization. (Email: wuchunming@cs.zju.edu.cn)

  • Corresponding author: Wei Ruan is the corresponding author
  • Received Date: 2020-12-12
  • Accepted Date: 2021-01-07
  • Available Online: 2021-11-05
  • With the recent increase in the number of Internet of Things (IoT) services, an intelligent scheduling strategy is needed to manage these services. In this paper, the problem of automatic choreography of microservices in IoT is explored. A type of reinforcement learning (RL) algorithm called TD3 is used to generate the optimal choreography policy under the framework of a softwaredefined network. The optimal policy is gradually reached during the learning procedure to achieve the goal, despite the dynamic characteristics of the network environment. The simulation results show that compared with other methods, the TD3 algorithm converges faster after a certain number of iterations, and it performs better than other non-RL algorithms by obtaining the highest reward. The TD3 algorithm can effciently adjust the traffc transmission path and provide qualified IoT services.
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