摘 要
随着信息技术的迅猛发展，云计算作为新一代计算模式，为用户提供按需获取计算资源的能力，然而资源需求的动态变化给云计算环境带来巨大挑战，弹性伸缩机制成为解决这一问题的关键。本研究旨在探索云计算环境下有效的弹性伸缩机制，以实现资源高效利用与用户服务质量保障之间的平衡。为此，构建了基于预测 - 决策 - 执行框架的弹性伸缩模型，融合机器学习算法对资源需求进行精准预测，依据预测结果制定合理的决策策略，并通过执行组件完成资源的自动调整。实验结果表明，所提出的机制能够有效应对负载波动，在保证服务性能的前提下，显著降低运营成本约30%，提高资源利用率25%以上。该研究创新性地将机器学习融入弹性伸缩过程，不仅提升了预测准确性，还增强了系统的自适应能力，为云计算环境中资源管理提供了新的思路和方法，对促进云计算技术的发展具有重要意义。
关键词：云计算 弹性伸缩机制 机器学习 资源需求预测
Abstract
With the rapid development of information technology, cloud computing, as a new generation of computing paradigms, provides users with the capability to obtain computing resources on demand. However, the dynamic changes in resource requirements pose significant challenges to cloud computing environments, making elastic scaling mechanisms crucial for addressing this issue. This study aims to explore effective elastic scaling mechanisms in cloud computing environments to achieve a balance between efficient resource utilization and quality of service assurance for users. To this end, an elastic scaling model based on a prediction-decision-execution fr amework was constructed, integrating machine learning algorithms for precise resource demand forecasting, formulating reasonable decision-making strategies according to the forecast results, and completing automatic resource adjustments through execution components. Experimental results demonstrate that the proposed mechanism can effectively handle load fluctuations, reducing operational costs by approximately 30% and improving resource utilization by over 25%, while ensuring service performance. This research innovatively incorporates machine learning into the elastic scaling process, not only enhancing prediction accuracy but also strengthening the system's adaptability, providing new approaches and methods for resource management in cloud computing environments, which is of great significance for promoting the development of cloud computing technology.
Keyword: Cloud computing The elastic scaling mechanism Machine learning Resource demand forecast
目 录
1 引言 1
2 弹性伸缩需求分析 1
2.1 云计算环境特征解析 1
2.2 用户负载模式研究 2
2.3 资源需求预测模型 2
2.4 弹性伸缩性能指标体系 3
3 弹性伸缩关键技术 3
3.1 自动化资源调度算法 3
3.2 动态资源分配策略 4
3.3 实时监控与反馈机制 4
3.4 弹性伸缩的安全保障 5
4 弹性伸缩实现方案 6
4.1 架构设计与系统框架 6
4.2 关键组件开发与集成 6
4.3 测试验证与优化调整 7
4.4 案例分析与应用实践 7
5 结论 8
参考文献 9
致谢 10
