云计算环境下的资源管理与调度 -计算机科学与技术专业

摘要

随着云计算技术的迅猛发展，云环境下的资源管理与调度成为提升云计算服务质量的关键问题。本研究旨在解决云计算环境中资源分配不均、调度效率低下等问题，以实现资源的高效利用和任务的快速响应。为此，提出了一种基于智能算法的动态资源管理与调度框架，该框架融合了强化学习与深度学习的优势，通过构建虚拟机预测模型来优化资源配置，并引入自适应调整机制以应对负载波动。实验结果表明，在多种典型应用场景下，所提方法能够显著降低平均等待时间约30%，提高资源利用率15%以上，同时有效减少了能源消耗。此外，该研究还设计了多目标优化策略，综合考虑性能、成本及能耗等多方面因素，为实际部署提供了理论依据和技术支持。本研究创新性地将人工智能技术应用于云计算资源管理领域，不仅丰富了现有理论体系，更为未来云服务架构的设计提供了新的思路与方向，对推动云计算产业的发展具有重要意义。

关键词：云计算资源管理智能调度算法强化学习与深度学习

Abstract

With the rapid development of cloud computing technology, resource management and scheduling in cloud environments have become critical issues for enhancing the quality of cloud computing services. This study aims to address problems such as uneven resource distribution and low scheduling efficiency in cloud computing environments to achieve efficient resource utilization and rapid task response. To this end, an intelligent algorithm-based dynamic resource management and scheduling fr amework is proposed, which integrates the advantages of reinforcement learning and deep learning. By constructing a virtual machine prediction model to optimize resource allocation and introducing an adaptive adjustment mechanism to handle load fluctuations, this fr amework seeks to improve operational efficiency. Experimental results demonstrate that the proposed method can significantly reduce average waiting time by approximately 30% and increase resource utilization by over 15%, while effectively reducing energy consumption across various typical application scenarios. Moreover, this research designs a multi-ob jective optimization strategy that comprehensively considers factors such as performance, cost, and energy consumption, providing theoretical basis and technical support for practical deployment. Innovatively applying artificial intelligence technologies to the field of cloud computing resource management, this study not only enriches the existing theoretical system but also offers new ideas and directions for the design of future cloud service architectures, playing a significant role in promoting the development of the cloud computing industry.

Keyword:Cloud Computing Resource Management Intelligent Scheduling Algorithm Reinforcement Learning And Deep Learning

1绪论 1

1.1云计算资源管理的背景与意义 1

1.2国内外研究现状综述 1

1.3研究方法与技术路线 2

2云计算资源模型构建 2

2.1资源抽象与分类体系 2

2.2弹性资源池设计原则 3

2.3资源分配策略分析 3

3资源调度算法优化 4

3.1调度目标与约束条件 4

3.2常见调度算法评析 5