舰船电子工程2025，Vol.45Issue(11)：128-132,5.DOI:10.3969/j.issn.1672-9730.2025.11.027
基于MDP与LSTM的云科研任务滚动调度方法
Rolling Horizon Scheduling for Scientific Cloud Workloads Via MDP and LSTM
王英 1王博 2姜高修 2朱理1
作者信息
- 1. 海军装备部 北京 100000
- 2. 南京电子工程研究所 南京 210000
- 折叠
摘要
Abstract
The explosive growth of computeintensive scientific workloads—such as AI training and bigdata analytics—poses triple challenges to cloud schedulers:highly dynamic task arrivals,pronounced resource heterogeneity and conflicting optimization objectives.To overcome the limited responsiveness and objective entanglement of existing approaches,this paper presents PRHS-MDP,a predictive rolling horizon scheduling algorithm formulated as a Markov Decision Process(MDP).An LSTM network is employed to forecast multistep node loads and augment the system state with foresight.A rollingwindow optimizer then maximizes a composite reward that jointly considers completion rate,mean response time,and energy consumption.Extensive simulations on a heterogeneous CloudSim testbed demonstrate that PRHS-MDP outperforms FIFO,MinMin,HEFT and PSO:task completion rate increases by 18%,mean response time drops by 23%,and total energy consumption is reduced by 11%,while preserving fast con-vergence and high robustness.The proposed method offers a practical and theoretically sound path towards intelligent,selfadaptive scheduling for scientific cloud platforms.关键词
云计算/任务调度/马尔可夫决策过程/负载预测Key words
cloud computing/task scheduling/Markov decision process/load prediction分类
信息技术与安全科学引用本文复制引用
王英,王博,姜高修,朱理..基于MDP与LSTM的云科研任务滚动调度方法[J].舰船电子工程,2025,45(11):128-132,5.
