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Technology of Network & Communication
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App. Res. of Comp. Printed Article
Vol. 40, 2023 No. 9
2803-2807,2814

Task offloading method based on deep reinforcement learning for vehicular edge computing

Guo Xiaodonga
Hao Sidab
Wang Lifangb
a. College of Electronic Information Engineering, b. College of Computer Science & Technology, Taiyuan University of Science & Technology, Taiyuan 030024, China
DOI: 10.19734/j.issn.1001-3695.2023.02.0027

Abstract

To meet the exploding demand for computational resources in vehicles, offloading computational tasks to edge servers is allowed in vehicular edge computing. But how to make offloading decision and computational resource allocation are still critical issues that need to be addressed. Moreover, task unloading of moving vehicles in continuous time is rarely mentioned, especially the randomness of vehicle task arrival is not considered enough. To address the above problems, this paper established a dynamic vehicle edge computing model and described this model as a Markov decision process in seven state two action spaces. Then this paper built a distributed deep reinforcement learning model to solve the problem. Furthermore, for the discrete-continuous hybrid decision problem causing poor results, this paper proposed a deep reinforcement learning algorithm for split-order decision making, which nested the input layer with the first-order decision network. Simulation results show that the proposed algorithm has significant advantages in terms of task completion rate, time delay, and reward compared to the comparison algorithm by maintaining a lower level of energy consumption. This paper provides an effective solution to the offloading decision and computational resource allocation problem in vehicle edge computing.

Foundation Support

国家自然科学基金资助项目(61876123)
山西省研究生教育改革项目(2021YJJG238,2021Y697)
太原科技大学博士启动基金资助项目(20212021)
大学生创新创业项目(20210499)

Publish Information

DOI: 10.19734/j.issn.1001-3695.2023.02.0027
Publish at: Application Research of Computers Printed Article, Vol. 40, 2023 No. 9
Section: Technology of Network & Communication
Pages: 2803-2807,2814
Serial Number: 1001-3695(2023)09-038-2803-05

Publish History

[2023-04-11] Accepted Paper
[2023-09-05] Printed Article

Cite This Article

郭晓东, 郝思达, 王丽芳. 基于深度强化学习的车辆边缘计算任务卸载方法 [J]. 计算机应用研究, 2023, 40 (9): 2803-2807,2814. (Guo Xiaodong, Hao Sida, Wang Lifang. Task offloading method based on deep reinforcement learning for vehicular edge computing [J]. Application Research of Computers, 2023, 40 (9): 2803-2807,2814. )

About the Journal

  • Application Research of Computers Monthly Journal
  • Journal ID ISSN 1001-3695
    CN  51-1196/TP

Application Research of Computers, founded in 1984, is an academic journal of computing technology sponsored by Sichuan Institute of Computer Sciences under the Science and Technology Department of Sichuan Province.

Aiming at the urgently needed cutting-edge technology in this discipline, Application Research of Computers reflects the mainstream technology, hot technology and the latest development trend of computer application research at home and abroad in a timely manner. The main contents of the journal include high-level academic papers in this discipline, the latest scientific research results and major application results. The contents of the columns involve new theories of computer discipline, basic computer theory, algorithm theory research, algorithm design and analysis, blockchain technology, system software and software engineering technology, pattern recognition and artificial intelligence, architecture, advanced computing, parallel processing, database technology, computer network and communication technology, information security technology, computer image graphics and its latest hot application technology.

Application Research of Computers has many high-level readers and authors, and its readers are mainly senior and middle-level researchers and engineers engaged in the field of computer science, as well as teachers and students majoring in computer science and related majors in colleges and universities. Over the years, the total citation frequency and Web download rate of Application Research of Computers have been ranked among the top of similar academic journals in this discipline, and the academic papers published are highly popular among the readers for their novelty, academics, foresight, orientation and practicality.

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  • The Second National Periodical Award 100 Key Journals
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  • the Core Journal of China (Peking University 2023 Edition)
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  • Chinese Science Citation Database (CSCD) Source Journals
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  • Journal of China Computer Federation
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  • Source Journal of China Academic Journals Comprehensive Evaluation Database
  • Source Journals of China Academic Journals (CD-ROM Version), China Journal Network
  • 2017-2019 China Outstanding Academic Journals with International Influence (Natural Science and Engineering Technology)
  • Source Journal of Top Academic Papers (F5000) Program of China's Excellent Science and Technology Journals
  • Source Journal of China Engineering Technology Electronic Information Network and Electronic Technology Literature Database
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  • Japan Science and Technology Agency (JST) Source Journal
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