Job Scheduling and Simulation in Cloud Based on Deep Reinforcement Learning

Authors

Qirui Li, 1.College of Computer Science, Guangdong University of Petrochemical Technology, Maoming 525000, China;Follow
Xinyi Peng, 2.School of Mathematical Sciences, South China Normal University, Guangzhou 510631, China;Follow

Abstract

Abstract: To solve the difficulty in job scheduling in the complex and transient multi-user, multi-queue, and multi-data-center cloud computing environment, this paper proposed a job scheduling method based on deep reinforcement learning. A system model of cloud job scheduling and its mathematical model were built, and an optimization goal consisting of transmission time, waiting time, and execution time was obtained. A job scheduling algorithm based on deep reinforcement learning was designed, and its state space, action space, and reward function were given. A simulated cloud job scheduler was designed and developed, and simulated scheduling experiments were conducted on it. The results show that compared with benchmark algorithms such as random scheduling, round-robin scheduling, firstfit, and optimal fit, the proposed algorithm could effectively reduce the overall makespan of the jobs.

Recommended Citation

Li, Qirui and Peng, Xinyi (2022) "Job Scheduling and Simulation in Cloud Based on Deep Reinforcement Learning," Journal of System Simulation: Vol. 34: Iss. 2, Article 8.
DOI: 10.16182/j.issn1004731x.joss.21-0337
Available at: https://dc-china-simulation.researchcommons.org/journal/vol34/iss2/8

First Page

258

Revised Date

2021-07-01

DOI Link

https://doi.org/10.16182/j.issn1004731x.joss.21-0337

Last Page

268

CLC

TP391.9

Recommended Citation

Qirui Li, Xinyi Peng. Job Scheduling and Simulation in Cloud Based on Deep Reinforcement Learning[J]. Journal of System Simulation, 2022, 34(2): 258-268.

Corresponding Author

Xinyi Peng,1742043887@qq.com

DOI

10.16182/j.issn1004731x.joss.21-0337