System Development & Application
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App. Res. of Comp. Printed Article
Vol. 41, 2024 No. 6
1797-1802

Scheduling of dynamic tasks in e-government clouds using deep reinforcement learning

Long Yujie1
Xiu Xi1
Huang Qing2
Huang Xiaomian3
Li Ying4
Wu Weigang1
1. School of Computer Science & Engineering, Sun Yat-Sen University, Guangzhou 510006, China
2. Guangzhou Digital Government Operations Center, Guangzhou 510635, China
3. Guangdong Yixun Technology Co. , Ltd. , Guangzhou 510635, China
4. Bingo Software Co. , Ltd. , Guangzhou 510663, China
DOI: 10.19734/j.issn.1001-3695.2023.10.0527

Abstract

The task scheduling of e-government cloud center has always been a complex problem. Most existing task scheduling solutions rely on expert knowledge and are not versatile enough to deal with dynamic cloud environment, which often leads to low resource utilization and degradation of quality-of-service, resulting in longer makespan. To address this issue, this paper proposed a deep reinforcement learning(DRL) scheduling algorithm based on the actor-critic(A2C) mechanism. Firstly, the actor network parameterized the policy and chose scheduling actions based on the current system state, while the critic network assigned scores to the current system state. Then, it updated the actor policy network using gradient ascent, utilizing the scores from the critic network to determine the effectiveness of actions. Finally, it conducted simulation experiments using real data from production datacenters. The results show that this method can improve resource utilization in cloud datacenters and reduce the makespan in comparison to the classic policy gradient algorithm and five commonly used heuristic task scheduling methods. This evidence suggests that the proposed method is superiorly adapted for the dynamic e-government clouds.

Publish Information

DOI: 10.19734/j.issn.1001-3695.2023.10.0527
Publish at: Application Research of Computers Printed Article, Vol. 41, 2024 No. 6
Section: System Development & Application
Pages: 1797-1802
Serial Number: 1001-3695(2024)06-028-1797-06

Publish History

[2024-01-15] Accepted Paper
[2024-06-05] Printed Article

Cite This Article

龙宇杰, 修熙, 黄庆, 等. 基于深度强化学习的电子政务云动态化任务调度方法 [J]. 计算机应用研究, 2024, 41 (6): 1797-1802. (Long Yujie, Xiu Xi, Huang Qing, et al. Scheduling of dynamic tasks in e-government clouds using deep reinforcement learning [J]. Application Research of Computers, 2024, 41 (6): 1797-1802. )

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  • 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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