Algorithm Research & Explore
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App. Res. of Comp. Printed Article
Vol. 42, 2025 No. 6
1676-1683

Joint offloading strategy for cloud manufacturing based on hybrid deep reinforcement learning in cloud-edge collaboration

Zhang Yaru
Guo Yinzhang
College of Computer Science & Technology, Taiyuan University of Science & Technology, Taiyuan 030024, China
DOI: 10.19734/j.issn.1001-3695.2024.11.0470

Abstract

To address the issue of real-time perception data from manufacturing resources being difficult to process promptly in a cloud-edge collaborative cloud manufacturing environment, considering uncertain factors such as the limited computing resources at the edge, dynamically changing network conditions, and task loads, this paper proposed a cloud-edge collaborative joint offloading strategy based on mixed-based deep reinforcement learning(M-DRL). Firstly, this strategy established a joint offloading model by combining discrete model offloading in the cloud with continuous task offloading at the edge. Secondly, this strategy defined the optimization problem as a MDP to minimize the total cost of delay and energy consumption over a period. Finally, this paper used the M-DRL algorithm, which utilized an integrated exploration strategy of DDPG and DQN and introduced a long short-term memory network(LSTM) into the network architecture, to solve this optimization problem. Simulation results show that compared with some existing offloading algorithms, the M-DRL method has good convergence and stability, and significantly reduces the total system cost. It provides an effective solution for the timely processing of manufacturing resource perception data.

Foundation Support

中央引导地方科技发展资金资助项目(YDZJSX20231A044)

Publish Information

DOI: 10.19734/j.issn.1001-3695.2024.11.0470
Publish at: Application Research of Computers Printed Article, Vol. 42, 2025 No. 6
Section: Algorithm Research & Explore
Pages: 1676-1683
Serial Number: 1001-3695(2025)06-010-1676-08

Publish History

[2025-03-10] Accepted Paper
[2025-06-05] Printed Article

Cite This Article

张亚茹, 郭银章. 基于混合深度强化学习的云制造云边协同联合卸载策略 [J]. 计算机应用研究, 2025, 42 (6): 1676-1683. (Zhang Yaru, Guo Yinzhang. Joint offloading strategy for cloud manufacturing based on hybrid deep reinforcement learning in cloud-edge collaboration [J]. Application Research of Computers, 2025, 42 (6): 1676-1683. )

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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