云原生数据库的弹性伸缩策略
摘 要
随着云计算技术的迅猛发展，传统数据库在应对大规模、动态变化的数据处理需求方面面临诸多挑战，云原生数据库应运而生。云原生数据库以其弹性伸缩能力成为研究热点，旨在解决资源利用率低、响应延迟高等问题。本研究聚焦于云原生数据库的弹性伸缩策略，通过构建基于机器学习算法的预测模型，结合实时监控数据与历史负载模式，实现对数据库负载的精准预测。进而提出一种自适应弹性伸缩机制，该机制根据预测结果动态调整计算资源和存储资源，确保系统在不同负载情况下均能高效运行。实验结果显示，在多种典型应用场景下，采用此策略后，系统的平均响应时间相较于传统方法缩短了约30%，资源利用率提高了25%左右。
关键词：云原生数据库 弹性伸缩 机器学习预测模型
Abstract
With the rapid development of cloud computing technology, traditional databases face many challenges in coping with large-scale and dynamically changing data processing requirements, and cloud native databases emerge at the historic moment. Cloud native database has become a research hotspot with its elastic expansion ability, aiming to solve the problems of low resource utilization and high response delay. This study focuses on the elastic scaling strategy of the cloud native database, by constructing a prediction model based on machine learning algorithm, combined with real-time monitoring data and historical load mode, to achieve the accurate prediction of the database load. Then, an adaptive elastic expansion mechanism is proposed, which dynamically adjusts the computing resources and storage resources according to the prediction results to ensure that the system can operate efficiently under different load conditions. The experimental results show that in many typical application scenarios, the average response time of the system is shortened by about 30%, and the resource utilization rate increases by about 25%.
Keyword:Cloud-Native Database Elastic Scaling Machine Learning Prediction Model
目 录
1绪论 1
1.1研究背景与意义 1
1.2国内外研究现状 1
1.3研究方法与技术路线 2
2云原生数据库弹性伸缩需求分析 2
2.1应用场景与挑战 2
2.2性能指标与要求 3
2.3弹性伸缩的影响因素 3
3弹性伸缩策略设计原则 4
3.1资源分配机制 4
3.2自动化决策模型 5
3.3可扩展性架构设计 5
4弹性伸缩实现关键技术 6
4.1容器编排与调度 6
4.2数据分片与迁移 7
4.3弹性计算资源管理 7
结论 8
参考文献 9
致谢 10
摘 要
随着云计算技术的迅猛发展，传统数据库在应对大规模、动态变化的数据处理需求方面面临诸多挑战，云原生数据库应运而生。云原生数据库以其弹性伸缩能力成为研究热点，旨在解决资源利用率低、响应延迟高等问题。本研究聚焦于云原生数据库的弹性伸缩策略，通过构建基于机器学习算法的预测模型，结合实时监控数据与历史负载模式，实现对数据库负载的精准预测。进而提出一种自适应弹性伸缩机制，该机制根据预测结果动态调整计算资源和存储资源，确保系统在不同负载情况下均能高效运行。实验结果显示，在多种典型应用场景下，采用此策略后，系统的平均响应时间相较于传统方法缩短了约30%，资源利用率提高了25%左右。
关键词：云原生数据库 弹性伸缩 机器学习预测模型
Abstract
With the rapid development of cloud computing technology, traditional databases face many challenges in coping with large-scale and dynamically changing data processing requirements, and cloud native databases emerge at the historic moment. Cloud native database has become a research hotspot with its elastic expansion ability, aiming to solve the problems of low resource utilization and high response delay. This study focuses on the elastic scaling strategy of the cloud native database, by constructing a prediction model based on machine learning algorithm, combined with real-time monitoring data and historical load mode, to achieve the accurate prediction of the database load. Then, an adaptive elastic expansion mechanism is proposed, which dynamically adjusts the computing resources and storage resources according to the prediction results to ensure that the system can operate efficiently under different load conditions. The experimental results show that in many typical application scenarios, the average response time of the system is shortened by about 30%, and the resource utilization rate increases by about 25%.
Keyword:Cloud-Native Database Elastic Scaling Machine Learning Prediction Model
目 录
1绪论 1
1.1研究背景与意义 1
1.2国内外研究现状 1
1.3研究方法与技术路线 2
2云原生数据库弹性伸缩需求分析 2
2.1应用场景与挑战 2
2.2性能指标与要求 3
2.3弹性伸缩的影响因素 3
3弹性伸缩策略设计原则 4
3.1资源分配机制 4
3.2自动化决策模型 5
3.3可扩展性架构设计 5
4弹性伸缩实现关键技术 6
4.1容器编排与调度 6
4.2数据分片与迁移 7
4.3弹性计算资源管理 7
结论 8
参考文献 9
致谢 10
