App. Res. of Comp. Accepted Paper
Vol. 41, 2024 No. 7

Drl-based joint optimization algorithm for service caching and task migration in multi-services mobile edge computing

Huang Hengjie1
Peng Ziyu2
Wang Gaocai2
1. Educational Technology Center, Yulin Normal University, Yulin Guangxi 537000, China
2. School of Computer & Electronic Information, Guangxi University, Nanning 530004, China
DOI: 10.19734/j.issn.1001-3695.2023.10.0538

Abstract

Multiple Services Mobile edge computing (MSs-MEC) can adaptively adjust service cache decisions according to needs, so that edge servers deployed on the user side can flexibly handle tasks of different service types. However, in practical applications, the successful migration of specific types of tasks depends on the early installation of the service environment. In addition, simultaneous task migration and service caching may result in time conflicts and computation delays. Therefore, in response to the above issues, this article first decouples task migration and service caching decisions. To address the shortcomings of deep reinforcement learning (DRL) in high-dimensional mixed decision spaces where performance improvement is not significant (such as low utilization rate during resource allocation) , DRL is combined with Transformer to learn from historical data and output task migration decisions for the current time slot and the next time slot, Ensure that tasks can be executed immediately when they reach the edge server. Secondly, in order to improve resource utilization in resource allocation problems, the problem is decomposed into continuous resource allocation problems and discrete task migration and service caching problems, and convex optimization techniques are used to solve the optimal decision for resource allocation. Extensive numerical results indicate that the proposed algorithm can effectively reduce the average completion delay of tasks compared to other baseline algorithms, at the same time, it also has excellent performance in resource utilization and stability.

Foundation Support

国家自然科学基金资助项目(62062007)
广西高校中青年教师科研基础能力提升项目(2020KY14020)
玉林师范学院高等教育本科教学改革工程项目(2022XJJGYB20)
玉林师范学院科研项目(2019YJKY15)

Publish Information

DOI: 10.19734/j.issn.1001-3695.2023.10.0538
Publish at: Application Research of Computers Accepted Paper, Vol. 41, 2024 No. 7

Publish History

[2024-01-22] Accepted Paper

Cite This Article

黄恒杰, 彭资馀, 王高才. MSs-MEC中基于DRL的服务缓存和任务迁移联合优化算法 [J]. 计算机应用研究, 2024, 41 (7). (2024-04-10). https://doi.org/10.19734/j.issn.1001-3695.2023.10.0538. (Huang Hengjie, Peng Ziyu, Wang Gaocai. Drl-based joint optimization algorithm for service caching and task migration in multi-services mobile edge computing [J]. Application Research of Computers, 2024, 41 (7). (2024-04-10). https://doi.org/10.19734/j.issn.1001-3695.2023.10.0538. )

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.

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