可再生能源2024，Vol.42Issue(9)：1170-1178,9.
提高数据中心供能中太阳能利用效率的云任务调度优化
Cloud tasks scheduling optimization for improving solar energy utilization efficiency in data center power supply
摘要
Abstract
Cloud computing demand has caused high energy consumption and carbon emission pressure while generating data center deployment applications,so the efficient utilization of renewable energy in cloud computing environment is proposed.Aiming at the intermittent non-stationary characteristics of solar energy,which is a specific form of renewable energy,we study the cloud task scheduling method to enhance the energy utilization in data center energy supply.DeepAR,a deep autoregressive model for predicting solar energy production capacity,is constructed to design cloud task scheduling strategies and algorithms by taking advantage of the flexible scheduling characteristics of delay-tolerant tasks and scheduled workloads in the time dimension,and simulation experiments are carried out using real task datasets and solar energy production capacity datasets by applying the GluonTS framework.The results show that the matching between computing load and solar power output is improved,and the utilization of solar power supply in data centers is enhanced.关键词
DeepAR模型/时间序列预测/太阳能/云任务/调度Key words
DeepAR model/time series prediction/solar energy/cloud tasks/scheduling分类
能源科技引用本文复制引用
党伟超,王振,薛颂东..提高数据中心供能中太阳能利用效率的云任务调度优化[J].可再生能源,2024,42(9):1170-1178,9.基金项目
太原科技大学博士科研启动基金(20202063) （20202063）
太原科技大学研究生教育创新项目(SY2022063) （SY2022063）
太原科技大学研究生联合培养示范基地项目(JD2022010). （JD2022010）
