Computer Science ›› 2021, Vol. 48 ›› Issue (7): 316-323.doi: 10.11896/jsjkx.200800095

• Computer Network • Previous Articles     Next Articles

Research Progress of Task Offloading Based on Deep Reinforcement Learning in Mobile Edge Computing

LIANG Jun-bin1,2, ZHANG Hai-han1,2, JIANG Chan3, WANG Tian-shu4   

  1. 1 School of Computer,Electronics and Information,Guangxi University,Nanning 530004,China
    2 Guangxi Key Laboratory of Multimedia Communication and Network Technology,Nanning 530004,China
    3 XingJian College of Science and Liberal Arts of Guangxi University,Nanning 530004,China
    4 Neusoft Group (Nanning) Co.,Ltd,Nanning 530007,China
  • Received:2020-08-16 Revised:2020-12-02 Online:2021-07-15 Published:2021-07-02
  • About author:LIANG Jun-bin,born in 1979,Ph.D,professor,Ph.D supervisor.His main research interests include wireless sensor networks,network deployment and optimization.(
    ZHANG Hai-han,born in 1993,postgraduate.His main research include wireless sensor networks and artificial intelligence.
  • Supported by:
    National Natural Science Foundation of China(61562005),Guangxi Key Research and Development Plan Project(AB19259006) and Natural Science Foundation of Guangxi(2019GXNSFAA185042,2018GXNSFBA281169).

Abstract: Mobile edge computing is a new type of network computing mode that has emerged in recent years.It allows server nodes with strong computing power and storage performance to be placed closer to the edge of the network of mobile devices (such as near base stations ),allowing mobile devices to offload tasks to edge devices for processing closely,thereby alleviates the disadvantages of traditional networks that have to spend a lot of time,energy and unsafely offload tasks to remote cloud platforms for processing due to weak computing and storage capabilities of mobile devices and limited energy.However,how to make a device that only has limited local information (such as the number of neighbors ) chooses to offload tasks to the local site according to the size and number of tasks,or chooses the mobile edge computing server with the optimal delay and energy consumption in a dynamic network where the wireless channel changes with time,to perform all or part of the task offloading,is a multi-objective programming problem and has a high degree of difficulty in solving.It is difficult to obtain better results with traditional optimization techniques(such as convex optimization).Deep reinforcement learning is a new type of artificial intelligence algorithm technology that combines deep learning and reinforcement learning.It can make more accurate decision-making results for complex collaboration,game and other issues.It has broad application prospects in many fields such as industry,agriculture and commerce.In recent years,It has become a new research trend to use deep reinforcement learning method to optimize task offloading in mobile edge computing networks.In the past three years,some researchers have conducted preliminary explorations on it,and achieved lower latency and energy consumption than using deep learning or reinforcement learning alone in the past,but there are still many shortcomings.In order to further advance the research in this field,this paper analyzes,compares and summarizes the domestic and foreign related work in recent years,summarizes their advantages and disadvantages,and discusses the possible future in research directions.

Key words: Deep learning, Deep reinforcement learning, Mobile edge computing, Offloading decision, Reinforcement learning, Task offloading

CLC Number: 

  • TP393
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