云平台的负载预测及弹性伸缩方案研究
Load prediction and elastic scaling solutions for cloud platforms
刘佳 1王冰 2王琛 1刘振博1
作者信息
- 1. 中铁信弘远(北京)软件科技有限责任公司,北京 100844
- 2. 中国铁路信息科技集团有限公司,北京 100844
- 折叠
摘要
为提高云平台的性能和资源利用率,文章提出一种基于ARMA-CNN-SVR的负载预测组合模型,通过融合多种预测模型的优点,提高预测云平台资源使用情况的准确率.基于该负载预测组合模型,进一步优化了弹性伸缩策略,有效解决资源调整的滞后性问题,增强了云平台的主动性和智能性,显著提升了资源利用率和服务质量.
Abstract
To improve the performance and resource utilization rate of cloud platforms,this paper proposed a load prediction combination model based on ARMA-CNN-SVR,improved the accuracy of predicting cloud platform resource usage by integrating the advantages of multiple prediction models.Based on this load forecasting combination model,the paper further optimized the elastic scaling strategy,effectively solved the lag problem of resource adjustment,enhanced the initiative and intelligence of the cloud platform,and significantly improved resource utilization rate and service quality.
关键词
云平台/负载预测/弹性伸缩/组合模型/资源利用率Key words
cloud platform/load prediction/elastic scaling/combination model/resource utilization rate引用本文复制引用
基金项目
中国国家铁路集团有限公司科技研究开发计划课题(P2021W009)
出版年
2024
NETL
NSTL
万方数据