Energy-aware systems for real-time job scheduling in cloud data centers: A deep reinforcement learning approach. (April 2022)

• Record Type: Journal Article
• Authors: Yan, Jingchen; Huang, Yifeng; Gupta, Aditya; Gupta, Anubhav; Liu, Cong; Li, Jianbin; Cheng, Long
• Is Part Of: Computers & electrical engineering. Volume 99(2022)
• Publication Date: 2022-04
• Subjects: Deep reinforcement learning -- Energy-aware scheduling -- Deep Q-learning -- Task scheduling -- Cloud computing -- Data centers -- QoS; Computer engineering -- Periodicals; Electrical engineering -- Periodicals; Electrical engineering -- Data processing -- Periodicals; Ordinateurs -- Conception et construction -- Périodiques; Électrotechnique -- Périodiques; Électrotechnique -- Informatique -- Périodiques; Computer engineering; Electrical engineering; Electrical engineering -- Data processing; Periodicals; Electronic journals; 621.302854
• DOI: 10.1016/j.compeleceng.2022.107688
• Languages: English
• ISSNs: 0045-7906

Abstract

Abstract: With the advantages such as high-performance, low-maintenance, and reliability, more and more companies are moving their computing infrastructures to the cloud. In the meantime, with the increasing number of users continuously submitting jobs to cloud, energy consumed by the current cloud data centers has become a major concern for cloud service providers, due to financial and environmental reasons. In this paper, we propose a deep reinforcement learning (DRL) approach to handle real-time jobs. Specifically, we focus on allocating incoming jobs to appropriate virtual machines (VMs) in a way that energy consumption can be optimized while high quality of service (QoS) can be achieved. We give the detailed design and implementation of our approach, and our experimental results demonstrate that the proposed method can achieve better performance in job success rate and average response time with less energy consumption than the current approaches, in the presence of different real-time cloud workloads.