Cost-aware cloud workflow scheduling using DRL and simulated annealing  

作　　者：Yan Gu Feng Cheng Lijie Yang Junhui Xu Xiaomin Chen Long Cheng 

机构地区：[1]School of Control and Computer Engineering,North China Electric Power University,Beijing 100026,China [2]School of Mathematics,Southwest Jiaotong University,Chengdu 610032,China [3]Department of Computer Science,University of Reading,Reading RG66AH,UK

出　　处：《Digital Communications and Networks》2024年第6期1590-1599,共10页数字通信与网络(英文版)

基　　金：supported by the Fundamental Research Funds for the Central Universities(2023JC004 and 2023YQ002)。

摘　　要：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.

关 键 词：Cloud computing Deep reinforcement learning Simulated annealing algorithm Job scheduling WORKFLOW 

分 类 号：TP18[自动化与计算机技术—控制理论与控制工程] TP393.09[自动化与计算机技术—控制科学与工程]