数据中心算能协同节能技术研究

Research on computing-energy collaborative energy-saving for data center

本文聚焦于数字化时代数据中心的能源消耗问题,并提出一种创新的算能协同节能策略,以响应全球对可持续性和环境保护的日益重视。数据中心作为高能耗设施,其节能技术研究具有重要的现实和经济意义。文章首先分析了当前数据中心的能耗现状,并重点介绍了PUE作为衡量数据中心能源效率的关键指标,指出数据中心资源利用率普遍较低的问题,提出了一种跨域算能协同节能方案,基于数据采集和处理技术实现跨域的数据互通、共享和联合分析。该方案结合神经网络模型和模拟退火算法,自动生成空调群控调优策略和算力资源配置策略,以提升数据中心整体能效。通过该方案,数据中心能够有效优化能源使用和算力资源分配,实现跨域协作下的能效提升。数据中心节能技术的应用与发展是一个复杂且持续的过程,需要不断创新与优化技术手段,提升智能化管理水平,并推动绿色能源的广泛应用。

This research focuses on the energy consumption of data centers in the digital age,and proposes an innovative energy-saving strategy to respond to the increasing global emphasis on sustainability and environmental protection.As a high-energy consumption facility,the research on energy-saving technology of data center has important practical and economic significance.This paper first analyzes the current energy consumption status of data centers,focuses on PUE as a key indicator to measure the energy eficiency of data centers,points out the problem of low resource utilization rate of data centers,and proposes a computing and energy collaborative energy-saving scheme to realize data exchange,sharing and joint analysis based on data acquisition and processing technology.The solution combines the neural network model and the simulated annealing algorithm to automatically generate the air conditioning optimization strategy and the computing resource allocation strategy to improve the overall energy efficiency of the data center.Through this solution,data centers can effectively improve energy efficiency.The application and development of energy-saving technology in data centers is a complex and continuous process,which requires continuous innovation and optimization,improvement of intelligent management level,and promotion of the wide application of green energy.