摘要
随着云计算技术的快速发展，软件服务在动态多变的云环境中面临着资源分配与性能优化的挑战，弹性伸缩策略成为提升系统效率和降低成本的关键手段。本研究旨在探索适用于云计算环境下的软件服务弹性伸缩策略，以实现资源的高效利用和用户体验的持续优化。为此，本文提出了一种基于预测模型与动态反馈机制相结合的弹性伸缩框架，该框架通过引入机器学习算法对负载变化进行精准预测，并结合实时监控数据调整资源分配策略。研究采用仿真实验与实际部署相结合的方式验证了所提方法的有效性，实验结果表明，相较于传统静态或简单阈值驱动的伸缩策略，新方法能够显著降低资源浪费，同时提高系统的响应速度和服务质量。此外，本文创新性地将多目标优化理论融入弹性伸缩决策过程，实现了性能与成本之间的动态平衡。研究的主要贡献在于提供了一种灵活且高效的弹性伸缩解决方案，为云计算环境下的软件服务设计与优化提供了理论支持和技术参考。
关键词：弹性伸缩策略；云计算环境；机器学习预测；多目标优化；资源分配
Abstract
With the rapid development of cloud computing technology, software services face challenges in resource allocation and performance optimization within a dynamic and ever-changing cloud environment. Elastic scaling strategies have become critical approaches for enhancing system efficiency and reducing costs. This study aims to explore elastic scaling strategies tailored for cloud computing environments to achieve efficient resource utilization and continuous user experience optimization. To this end, a novel elastic scaling fr amework is proposed, which integrates predictive modeling with a dynamic feedback mechanism. By incorporating machine learning algorithms, this fr amework enables precise prediction of load variations and adjusts resource allocation strategies based on real-time monitoring data. The effectiveness of the proposed method is validated through a combination of simulation experiments and practical deployment. Experimental results demonstrate that, compared to traditional static or simple threshold-driven scaling strategies, the new approach significantly reduces resource wastage while improving system response time and service quality. Additionally, this study innovatively incorporates multi-ob jective optimization theory into the elastic scaling decision-making process, achieving dynamic balance between performance and cost. The primary contribution of this research lies in providing a flexible and efficient elastic scaling solution, offering theoretical support and technical references for the design and optimization of software services in cloud computing environments.
Keywords：Elastic Scaling Strategy; Cloud Computing Environment; Machine Learning Prediction; Multi-ob jective Optimization; Resource Allocation
目 录
摘要 I
Abstract II
一、绪论 1
(一) 云计算与弹性伸缩的研究背景 1
(二) 弹性伸缩策略的国内外研究现状 1
(三) 本文研究方法与技术路线 2
二、云计算环境下的弹性需求分析 2
(一) 软件服务的动态负载特性 2
(二) 弹性伸缩的关键需求要素 3
(三) 用户行为对弹性策略的影响 3
三、弹性伸缩策略的设计与实现 4
(一) 弹性伸缩的核心算法研究 4
(二) 自动化决策机制的设计 4
(三) 策略实施中的资源分配优化 5
四、实验验证与性能评估 6
(一) 实验环境与数据集构建 6
(二) 弹性伸缩策略的测试与分析 6
(三) 性能评估与结果讨论 7
结 论 8
参考文献 9
