Abstract:

As disk is one of the most important data storage device, it is significant to improve disks’ reliability and data availability. Modern disks adopt the SMART protocol to monitor the internal operating status. We employ machine learning methods, including backpropagation neural networks, decision tree, supported vector machine and nave Bayes to analyze the SMART data of disks, which can predict disk failures. Real SMART data of disks are used in experiments to validate and analyze the effectiveness of those methods, and the effectiveness of different methods is compared. The results show that the decision tree method has best prediction rate while the supported vector machine method has the lowest false alarm rate.

Key words: disk;failure prediction;machine learning