Cost-aware cloud workflow scheduling using DRL and simulated annealing

IF 7.5 2区计算机科学 Q1 TELECOMMUNICATIONS

Digital Communications and Networks Pub Date : 2024-12-01 DOI:10.1016/j.dcan.2023.12.009

Yan Gu , Feng Cheng , Lijie Yang , Junhui Xu , Xiaomin Chen , Long Cheng

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引用次数: 0

Abstract

Cloud workloads are highly dynamic and complex, making task scheduling in cloud computing a challenging problem. While several scheduling algorithms have been proposed in recent years, they are mainly designed to handle batch tasks and not well-suited for real-time workloads. To address this issue, researchers have started exploring the use of Deep Reinforcement Learning (DRL). However, the existing models are limited in handling independent tasks and cannot process workflows, which are prevalent in cloud computing and consist of related subtasks. In this paper, we propose SA-DQN, a scheduling approach specifically designed for real-time cloud workflows. Our approach seamlessly integrates the Simulated Annealing (SA) algorithm and Deep Q-Network (DQN) algorithm. The SA algorithm is employed to determine an optimal execution order of subtasks in a cloud server, serving as a crucial feature of the task for the neural network to learn. We provide a detailed design of our approach and show that SA-DQN outperforms existing algorithms in terms of handling real-time cloud workflows through experimental results.

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利用 DRL 和模拟退火进行成本感知云工作流调度

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来源期刊

Digital Communications and Networks Computer Science-Hardware and Architecture

CiteScore

12.80

自引率

5.10%

发文量

915

审稿时长

30 weeks

期刊介绍： Digital Communications and Networks is a prestigious journal that emphasizes on communication systems and networks. We publish only top-notch original articles and authoritative reviews, which undergo rigorous peer-review. We are proud to announce that all our articles are fully Open Access and can be accessed on ScienceDirect. Our journal is recognized and indexed by eminent databases such as the Science Citation Index Expanded (SCIE) and Scopus. In addition to regular articles, we may also consider exceptional conference papers that have been significantly expanded. Furthermore, we periodically release special issues that focus on specific aspects of the field. In conclusion, Digital Communications and Networks is a leading journal that guarantees exceptional quality and accessibility for researchers and scholars in the field of communication systems and networks.