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Research of Offloading Decision and Resource Scheduling in Edge Computing Based on Deep Reinforcement Learning

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Smart Grid and Internet of Things (SGIoT 2020)

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Abstract

The increasing scale of the IOT poses challenges to the energy consumption, transmission bandwidth and processing delay of centralized cloud computing data centers. The cloud computing data centers is moving from the center of the network to edge nodes with lower latency, namely, edge computing. Meanwhile, it can meet the needs of users for real-time services. In the field of edge computing, offloading decision and resource scheduling are the hot-spot issues. As for offloading decision and resource scheduling problems of single-cell multi-user partial offloading, the system model is also firstly established from four aspects: network architecture, application type, local computing and offloading computing. Based on the system model, the optimization problem of resource scheduling is modeled, where the solution is hard to be found. Thus, the deep reinforcement learning method based on policy gradient is selected to establish the SPBDDPG algorithm that can solve the problem. Then, in order to solve the practical problems, the SPBDDPG algorithm is set up with the state and action for iteration, as well as the environment for generating new state and feedback reward value. Finally, an appropriate iteration step is written for the edge computing resource scheduling problem by combining with the original deep reinforcement learning algorithm. We also evaluate the proposed approaches by relevant experiments. The complexity and effectiveness of the results are validated.

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Acknowledgement

The authors gratefully acknowledge the support and financial assistance provided by the National Natural Science Foundation under Grant No. 61772064 and 61701019, the National Key R&D Program of China under Grant No. 2018YFC0831900. The authors thank the anonymous reviewers who provided constructive feedback on earlier work of this paper.

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Authors and Affiliations

  1. Department of Software Engineering, Key Laboratory of Communication and Information Systems, Beijing Municipal Commission of Education, Beijing Jiaotong University, Beijing, 100044, China

    Zhen-Jiang Zhang

  2. Department of Electronic and Information Engineering, Key Laboratory of Communication and Information Systems, Beijing Municipal Commission of Education, Beijing Jiaotong University, Beijing, 100044, China

    Tong Wu, Zhiyuan Li, Bo Shen & Jian Li

  3. School of Computer and Information Technology, Beijing Jiaotong University, Beijing Municipal Commission of Education, Beijing, 100044, China

    Naiyue Chen

Authors
  1. Zhen-Jiang Zhang
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  2. Tong Wu
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  3. Zhiyuan Li
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  4. Bo Shen
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  5. Naiyue Chen
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  6. Jian Li
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Corresponding author

Correspondence to Zhen-Jiang Zhang .

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Editors and Affiliations

  1. National Chiao Tung University, Hsinchu, Taiwan

    Yi-Bing Lin

  2. National Changhua University of Education, Changhua, Taiwan

    Der-Jiunn Deng

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© 2021 ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering

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Zhang, ZJ., Wu, T., Li, Z., Shen, B., Chen, N., Li, J. (2021). Research of Offloading Decision and Resource Scheduling in Edge Computing Based on Deep Reinforcement Learning. In: Lin, YB., Deng, DJ. (eds) Smart Grid and Internet of Things. SGIoT 2020. Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, vol 354. Springer, Cham. https://doi.org/10.1007/978-3-030-69514-9_1

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  • DOI: https://doi.org/10.1007/978-3-030-69514-9_1

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-69513-2

  • Online ISBN: 978-3-030-69514-9

  • eBook Packages: Computer ScienceComputer Science (R0)

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