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Data Mining Methods Application for Increasing the Data Storage Systems Fault-Tolerance

This study is dedicated to the application the data mining methods for prediction the disks faults in data storage. The data set of the company Backblaze that has more than 45 disks characteristics was chosen. As the most important variables that can detect the probable fault were chosen 5 SMART-characteristics that indicated that the fault can happen really soon. Different data mining methods, such as logistic regression, random forest and support vector machines, were used for checking the effectiveness of fault-detecting and using the prevention means. These methods were applied for prediction the fact of the disk fault. Random forest was detected as the most precise model. Next step of the modelling was calculating the risk of the storage volume losing by the mathematical expectation and variance of the lost memory. Also it was forecast on the test data sample the quantity of the possible lost storage space and needed memory space for its reservation and prevention the information losing, providing the fault-tolerance of the data storage.