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Technology of Network & Communication
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App. Res. of Comp. Printed Article
Vol. 39, 2022 No. 12
3760-3764

Task secondary application offloading strategy based on deep reinforcement learning in cloud-edge collaborative computing

Yang Kunlun1
Wang Maoli2
Wang Yalin2
Ma Xu2
1. School of Computer Science, Qufu Normal University, Rizhao Shandong 276826, China
2. School of Cyber Science & Engineering, Qufu Normal University, Qufu Shandong 273165, China
DOI: 10.19734/j.issn.1001-3695.2022.04.0220

Abstract

Existing task offloading strategy usually makes offloading decision within one time slot without considering the internal relationship between multiple offload time slots, so they cannot be offloaded according to the actual needs of tasks. To solve this problem, this paper proposed a task secondary application offloading strategy based on deep Q network(DQN-TSAO). Firstly, this paper introduced a three-layer of cloud-edge-end architecture that supported task secondary application offloading, and established priority model, delay model and energy consumption model for task offloading. Secondly, aiming at minimizing system energy consumption, it transformed the energy consumption optimization problem into a Markov decision process problem of maximum cumulative offloading reward. Finally, DQN-TSAO algorithm could extract the task offload characteristics of each time slot, which enabled the task to obtain the optimal offloading decision of multiple time slots in the continuous interaction with the environment. Simulation results validate that DQN-TSAO algorithm can effectively reduce the total energy consumption of the system in a period of time.

Foundation Support

国家自然科学基金资助项目(61802227)
山东省农业重大应用技术创新项目(SD2019NJ007)

Publish Information

DOI: 10.19734/j.issn.1001-3695.2022.04.0220
Publish at: Application Research of Computers Printed Article, Vol. 39, 2022 No. 12
Section: Technology of Network & Communication
Pages: 3760-3764
Serial Number: 1001-3695(2022)12-037-3760-05

Publish History

[2022-07-20] Accepted Paper
[2022-12-05] Printed Article

Cite This Article

杨昆仑, 王茂励, 王亚林, 等. 云边协同计算中基于深度强化学习的任务二次申请卸载策略 [J]. 计算机应用研究, 2022, 39 (12): 3760-3764. (Yang Kunlun, Wang Maoli, Wang Yalin, et al. Task secondary application offloading strategy based on deep reinforcement learning in cloud-edge collaborative computing [J]. Application Research of Computers, 2022, 39 (12): 3760-3764. )

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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