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Algorithm Research & Explore
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App. Res. of Comp. Printed Article
Vol. 42, 2025 No. 2
486-493

Deep reinforcement learning based task offloading decision and resource allocation method in edge and cloud environments

He Dahanga
Wang Yub
Zuo Liyuna
a. College of Computer, b. Network & Education Information Technology Center, Guangdong University of Petrochemical Technology, Maoming Guangdong 525000, China
DOI: 10.19734/j.issn.1001-3695.2024.07.0228

Abstract

Edge computing allows Internet of Things devices to offload tasks to the edge and cloud environments for execution to meet the task's demand for resources. Due to the highly stochastic and dynamic nature of edge and cloud environments, heuristic algorithms and Q-table based reinforcement learning algorithms struggle to achieve efficient offloading decisions for heterogeneous tasks. Therefore, this paper proposed a novel deep reinforcement learning algorithm called novel dueling and double deep Q network(ND3QN) for efficient offloading of tasks and resource allocation in edge and cloud environments. ND3QN jointly optimized the completion time and cost, and innovatively constructed state containing dynamic information about the environments. It designed reward functions guiding the training of the algorithm efficiently and realized fine-grained offloading, i. e., the offloading of tasks to the servers' virtual machines. The experimental results show that ND3QN has significant differences in convergence speed and convergence values under different exploration rates and learning rates, and outperforms the baseline algorithms in terms of task discard rate, completion time and cost. The ablation experiments prove the effectiveness of the state and reward function improvements. Therefore, ND3QN can effectively improve the efficiency of task offloading and resource allocation in edge and cloud environments.

Foundation Support

广东省自然科学基金资助项目(2024A1515010144)
广东省普通高校重点领域专项(2023ZDZX3013)
茂名绿色化工研究院扬帆计划资助项目(MMGCIRI-2022YFJH-Y-012)

Publish Information

DOI: 10.19734/j.issn.1001-3695.2024.07.0228
Publish at: Application Research of Computers Printed Article, Vol. 42, 2025 No. 2
Section: Algorithm Research & Explore
Pages: 486-493
Serial Number: 1001-3695(2025)02-022-0486-08

Publish History

[2025-02-05] Printed Article

Cite This Article

何达航, 王昱, 左利云. 边云环境中基于深度强化学习的任务卸载和资源分配方法 [J]. 计算机应用研究, 2025, 42 (2): 486-493. (He Dahang, Wang Yu, Zuo Liyun. Deep reinforcement learning based task offloading decision and resource allocation method in edge and cloud environments [J]. Application Research of Computers, 2025, 42 (2): 486-493. )

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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