摘 要
随着云计算技术的迅猛发展，云平台面临着日益复杂的资源管理挑战。为提高资源利用率和服务质量，本研究聚焦于云计算环境下的资源动态分配与负载均衡问题，旨在构建一种高效、智能的资源管理机制。通过分析现有资源分配算法和负载均衡策略的局限性，提出了一种基于预测模型与自适应调整机制相结合的新型资源调度框架。该框架利用机器学习算法对用户需求进行精准预测，并结合实时监控数据实现动态资源分配。实验结果表明，所提出的方案在响应时间、资源利用率等方面较传统方法有显著提升，特别是在高并发场景下表现出色。此外，本研究还引入了多目标优化理论，解决了不同业务类型之间的资源竞争问题，实现了系统整体性能的最优化。创新点在于将预测模型融入资源调度过程，提高了系统的前瞻性和灵活性；同时设计了自适应调整机制，确保系统能够根据实际运行状况自动优化配置。这一研究成果不仅为云计算平台提供了有效的资源管理手段，也为相关领域的研究提供了新的思路和方法。
关键词：云计算资源管理 动态资源分配 负载均衡
Abstract
With the rapid development of cloud computing technology, cloud platforms are facing increasingly complex resource management challenges. To improve resource utilization and service quality, this study focuses on dynamic resource allocation and load balancing in cloud computing environments, aiming to construct an efficient and intelligent resource management mechanism. By analyzing the limitations of existing resource allocation algorithms and load balancing strategies, a novel resource scheduling fr amework combining predictive modeling with adaptive adjustment mechanisms is proposed. This fr amework employs machine learning algorithms to accurately predict user demands and integrates real-time monitoring data to achieve dynamic resource allocation. Experimental results demonstrate that the proposed approach significantly enhances response time and resource utilization compared to traditional methods, particularly excelling in high-concurrency scenarios. Additionally, multi-ob jective optimization theory is introduced to address resource contention among different types of services, optimizing overall system performance. The innovation lies in integrating predictive models into the resource scheduling process, enhancing the system's foresight and flexibility; meanwhile, an adaptive adjustment mechanism is designed to ensure automatic configuration optimization based on actual operational conditions. This research not only provides effective resource management solutions for cloud computing platforms but also offers new insights and methodologies for related fields.
Keyword:Cloud Computing Resource Management Dynamic Resource Allocation Load Balancing
目 录
1绪论 1
1.1云计算资源分配与负载均衡的研究背景 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云计算资源动态分配模型 2
2.1资源分配的基本概念与挑战 2
2.2动态资源分配算法分析 3
2.3基于预测的资源预分配策略 3
3负载均衡机制设计 4
3.1负载均衡的关键问题 4
3.2分布式负载均衡算法 4
3.3弹性负载均衡架构设计 5
4实验验证与性能评估 6
4.1实验环境搭建与数据采集 6
4.2性能指标体系构建 6
4.3结果分析与优化建议 7
结论 7
参考文献 9
致谢 10
