首页|Intelligent Orchestrating of IoT Microservices Based on Reinforcement Learning

Intelligent Orchestrating of IoT Microservices Based on Reinforcement Learning

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With the recent increase in the number of Internet of things(IoT)services,an intelligent schedul-ing strategy is needed to manage these services.In this paper,the problem of automatic choreography of mi-croservices 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 gradu-ally 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 bet-ter than other non-RL algorithms by obtaining the highest reward.The TD3 algorithm can effciently adjust the traffic transmission path and provide qualified IoT services.

Internet of thingsMicroserviceTraffc schedulingReinforcement learning

WU Yuqin、SHEN Congqi、CHEN Shuhan、WU Chunming、LI Shunbin、WEI Ruan

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College of Computer Science and Technology,Zhejiang University,Hangzhou 310027,China

College of Information and Electrical Engineering,Ningde Normal University,Ningde 352100,China

Zhejiang Lab,Hangzhou 310012,China

College of Control Science and Engineering,Zhejiang University,Hangzhou 310027,China

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国家重点研发计划浙江省重点研发计划Fujian Natural Science FoundationNingde Normal University Innovation Team ProgramFundamental Research Funds for the Central Universities(Zhejiang University NGICS Platform)Major Scientific Project of Zhejiang Lab

2018YFB21004042020C010772020J014312018T042018FD0ZX01

2022

10.1049/cje.2020.00.417

电子学报(英文)

电子学报(英文)

CSTPCDSCIEI
ISSN：1022-4653
年,卷(期)：2022.31(5)
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