一种资源与服务性能关系的建模方法  

A Novel Modeling Method for Relationships Between Resources and Service Performance

作　　者：张斌[1] 王林[1] 赵秀涛[1] 张长胜[1] 

机构地区：[1]东北大学信息科学与工程学院,辽宁沈阳110819

出　　处：《东北大学学报（自然科学版）》2015年第6期773-776,共4页Journal of Northeastern University(Natural Science)

基　　金：宁夏回族自治区自然科学基金资助项目(NZ13265);中央高校基本科研业务费专项资金资助项目(N120804001;N120604003);沈阳市科技基金资助项目(F12-277-1-80);国家科技支撑计划项目(2014BAI17B00)

摘　　要：获取资源与服务性能的关系模型是在云环境中为服务合理分配虚拟资源的关键.然而,训练数据的规模往往显著影响这种非线性关系模型的准确率.针对现有方法不足,提出了将协同过滤推荐(CFR)和支持向量回归(SVR)相结合的服务性能动态建模方法(CSDM).该方法在服务部署与运行时同时训练两种模型,并选择二者中MAE占优的性能模型预测给定资源状态下的服务性能,从而保证预测精度.同时,CSDM引入择优阈值以降低模型训练代价.实验表明,CSDM在不同规模的训练数据上均有较高的预测准确率,且择优阈值对预测精度和建模效率具有显著影响.The relationship model between resources and service performance is a key to the proper virtual resource allocation for services in cloud environment. However, the accuracy of these non-linear relationship models is usually significantly influenced by the scale of training data. Aiming at the shortcomings of related work, a dynamic service performance modeling method named CSDM, which combines collaborative filtering recommendation and support vector regression, was proposed. In CSDM, for better accuracy, both performance models were trained at service deployment time and runtime, and the one with lower MAE was selected to estimate the performance under given resource status. In addition, a merit-based threshold was introduced to reduce training costs of performance models. The experimental results showed that CSDM had higher accuracy on different scales of training data, and the merit-based threshold had a significant effect on the prediction accuracy as well as the modeling efficiency.

关 键 词：云服务 性能模型 资源状态 协同过滤推荐 支持向量回归 

分 类 号：TP311.5[自动化与计算机技术—计算机软件与理论]