云计算中的能耗管理优化策略
摘 要
随着云计算技术的快速发展，数据中心能耗问题日益凸显，成为制约其可持续发展的关键因素。为此，本文旨在探索一种高效的能耗管理优化策略，以降低云计算环境下的能源消耗并提升资源利用率。研究基于动态任务调度与虚拟机整合技术，提出了一种结合预测模型和智能算法的能耗优化框架。通过引入深度学习方法对工作负载进行精准预测，并结合遗传算法实现任务分配与资源调度的全局优化，从而有效减少空闲服务器的数量及不必要的电力损耗。实验结果表明，该策略在多种典型场景下可平均降低能耗约25%，同时确保服务性能满足用户需求。本研究的主要贡献在于将人工智能技术与云计算能耗管理深度融合，为实现绿色计算提供了新思路，具有重要的理论价值和实际应用前景。
关键词：云计算能耗管理；深度学习预测；遗传算法优化
Abstract
With the rapid development of cloud computing technology, the energy consumption issue in data centers has become increasingly prominent, serving as a critical factor that constrains their sustainable development. To address this challenge, this study aims to explore an efficient energy consumption management optimization strategy to reduce energy usage and enhance resource utilization in cloud computing environments. Based on dynamic task scheduling and virtual machine consolidation techniques, a novel energy consumption optimization fr amework is proposed, which integrates predictive modeling with intelligent algorithms. By incorporating deep learning methods for precise workload prediction and combining genetic algorithms for global optimization of task allocation and resource scheduling, the proposed approach effectively minimizes the number of idle servers and unnecessary power losses. Experimental results demonstrate that this strategy can achieve an average reduction of approximately 25% in energy consumption across various typical scenarios while ensuring service performance meets user requirements. The primary contribution of this research lies in the deep integration of artificial intelligence technologies with cloud computing energy management, offering new insights into achieving green computing with significant theoretical value and practical application potential.
Keywords: Cloud Computing Energy Management；Deep Learning Prediction；Genetic Algorithm Optimization
目 录
引言 1
一、云计算能耗管理现状分析 1
（一）云计算能耗的主要来源 1
（二）当前能耗管理技术概述 1
（三）能耗管理存在的挑战 2
二、能耗优化策略的理论基础 2
（一）能耗优化的基本原理 2
（二）数据中心能效评估模型 3
（三）绿色计算与能耗优化的关系 3
三、动态资源调度与能耗优化 4
（一）资源调度对能耗的影响 4
（二）动态调度算法的设计原则 4
（三）实时调度中的能耗优化实践 4
四、能耗管理优化的技术实现路径 5
（一）虚拟化技术在能耗管理中的应用 5
（二）边缘计算对能耗优化的贡献 5
（三）智能化监控与能耗预测方法 6
结 论 6
致 谢 8
参考文献 9
摘 要
随着云计算技术的快速发展，数据中心能耗问题日益凸显，成为制约其可持续发展的关键因素。为此，本文旨在探索一种高效的能耗管理优化策略，以降低云计算环境下的能源消耗并提升资源利用率。研究基于动态任务调度与虚拟机整合技术，提出了一种结合预测模型和智能算法的能耗优化框架。通过引入深度学习方法对工作负载进行精准预测，并结合遗传算法实现任务分配与资源调度的全局优化，从而有效减少空闲服务器的数量及不必要的电力损耗。实验结果表明，该策略在多种典型场景下可平均降低能耗约25%，同时确保服务性能满足用户需求。本研究的主要贡献在于将人工智能技术与云计算能耗管理深度融合，为实现绿色计算提供了新思路，具有重要的理论价值和实际应用前景。
关键词：云计算能耗管理；深度学习预测；遗传算法优化
Abstract
With the rapid development of cloud computing technology, the energy consumption issue in data centers has become increasingly prominent, serving as a critical factor that constrains their sustainable development. To address this challenge, this study aims to explore an efficient energy consumption management optimization strategy to reduce energy usage and enhance resource utilization in cloud computing environments. Based on dynamic task scheduling and virtual machine consolidation techniques, a novel energy consumption optimization fr amework is proposed, which integrates predictive modeling with intelligent algorithms. By incorporating deep learning methods for precise workload prediction and combining genetic algorithms for global optimization of task allocation and resource scheduling, the proposed approach effectively minimizes the number of idle servers and unnecessary power losses. Experimental results demonstrate that this strategy can achieve an average reduction of approximately 25% in energy consumption across various typical scenarios while ensuring service performance meets user requirements. The primary contribution of this research lies in the deep integration of artificial intelligence technologies with cloud computing energy management, offering new insights into achieving green computing with significant theoretical value and practical application potential.
Keywords: Cloud Computing Energy Management；Deep Learning Prediction；Genetic Algorithm Optimization
目 录
引言 1
一、云计算能耗管理现状分析 1
（一）云计算能耗的主要来源 1
（二）当前能耗管理技术概述 1
（三）能耗管理存在的挑战 2
二、能耗优化策略的理论基础 2
（一）能耗优化的基本原理 2
（二）数据中心能效评估模型 3
（三）绿色计算与能耗优化的关系 3
三、动态资源调度与能耗优化 4
（一）资源调度对能耗的影响 4
（二）动态调度算法的设计原则 4
（三）实时调度中的能耗优化实践 4
四、能耗管理优化的技术实现路径 5
（一）虚拟化技术在能耗管理中的应用 5
（二）边缘计算对能耗优化的贡献 5
（三）智能化监控与能耗预测方法 6
结 论 6
致 谢 8
参考文献 9
