ISSN 1000-1239 CN 11-1777/TP

Journal of Computer Research and Development ›› 2020, Vol. 57 ›› Issue (7): 1539-1554.doi: 10.7544/issn1000-1239.2020.20190291

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

Lu Haifeng, Gu Chunhua, Luo Fei, Ding Weichao, Yang Ting, Zheng Shuai   

  1. (School of Information Science and Engineering, East China University of Science and Technology, Shanghai 200237)
  • Online:2020-07-01
  • Supported by: 
    This work was supported by the National Natural Science Foundation of China (61472139) and the Educational Teaching Law and Method Research Project of East China University of Science and Technology (ZH1726107).

Abstract: In the mobile edge computing, the local device can offload tasks to the server near the edge of the network for data storage and computation processing, thereby reducing the delay and power consumption of the service. Therefore, the task offloading decision has great research value. This paper first constructs an offloading model with multi-service nodes and multi-dependencies within mobile tasks in large-scale heterogeneous mobile edge computing. Then, an improved deep reinforcement learning algorithm is proposed to optimize the task offloading strategy by combining the actual application scenarios of mobile edge computing. Finally, the advantages and disadvantages of each offloading strategy are analyzed by comprehensively comparing the energy consumption, cost, load balancing, delay, network usage and average execution time. The simulation results show that the improved HERDRQN algorithm based on long short-term memory (LSTM) network and HER (hindsight experience replay) has good effects on energy consumption, cost, load balancing and delay. In addition, this paper uses various algorithm strategies to offload a certain number of applications, and compares the number distribution of heterogeneous devices under different CPU utilizations to verify the relationship between the offloading strategy and each evaluation index, so as to prove that the strategy generated by HERDRQN algorithm is scientific and effective in solving the task offloading problem.

Key words: mobile edge computing, task offloading, deep reinforcement learning, long short-term memory (LSTM) network, hindsight experience replay (HER)

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