南京大学学报：自然科学版2012，Vol.48Issue(2)：123-132,10.
基于回归树与K-最近邻交互模型的存储设备性能预测
An interactive model based on regression tree and K-nearest neighbor for storage device performance prediction
摘要
Abstract
Storage device performance prediction is a significant element of self-managed storage systems and application planning tasks, such as data assignment. The traditional methods for storage device performance prediction, such as accurate simulations and analytic models, needs sufficient expertise about storages. As the storage devices are becoming more and more high-end and complex, the accurate simulations and analytic models are not available. Compared with traditional methods, the machine learning methods consider the storage devices as black boxes, and needs no information about the internal components or algorithms of those storage devices. So machine learning methods are more appropriate for the trend of current storage devices development. Classification and regression tree（CART） method for modelling storage devices is simple. This work explores an interactive model based on regression tree and K-nearest neighbor algorithm to improve the machine learning method. Experiments show that our proposed model has a higher prediction precise and a better stability than regression tree or KNN. In our experiments, we found out that the caching effect is very important. We improved the method of workload characterization considering caching effect, which makes a substantial difference on prediction accuracy.关键词
回归模型/回归树/K-最近邻/特征权重/存储设备性能预测Key words
regression/regression tree/K nearest neighbors/feature weighting/storage device performance prediction分类
信息技术与安全科学引用本文复制引用
郭昌辉,刘贵全,张磊..基于回归树与K-最近邻交互模型的存储设备性能预测[J].南京大学学报：自然科学版,2012,48(2):123-132,10.基金项目
中央高校基本科研基金 （）
