关键词: COG-OS框架, 自我监测分析和报告技术, K-均值, 少量样本合成过采样技术, LIBSVM, 支持向量机

Abstract: The hard disk of cloud computing platform is not reliable. This paper proposed to use Self-Monitoring Analysis and Reporting Technology (SMART) log to predict hard disk failure based on Classification using lOcal clusterinG with Over-Sampling (COG-OS) framework. First, faultless hard disks were divided into multiple disjoint sample subsets by using DBScan or K-means clustering algorithm. And then these subsets and another sample set of faulty hard disks were mixed, and Synthetic Minority Over-sampling TEchnique (SMOTE) was used to make the overall sample set tend to balance. At last, faulty hard disks was predicted by using LIBSVM classification algorithm. The experimental results show that the method is feasible. COG-OS improves SMOTE+Support Vector Machine (SVM) on faulty hard disks' recall and overall performance, when using K-means method to divide samples of faultless hard disks and using LIBSVM method with Radial Basis Function (RBF) kernel to predict faulty hard disks.

Key words: Classification using lOcal clusterinG with Over-Sampling (COG-OS) framework, Self-Monitoring Analysis and Reporting Technology (SMART), K-means, Synthetic Minority Over-sampling TEchnique (SMOTE), LIBSVM, Support Vector Machine (SVM)