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A Multivariate Time Series Streaming Classifier for Predicting Hard Drive Failures [Application Notes]
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DOI：10.1109/MCI.2021.3129962.png)
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Digital data storage systems such as hard drives can suffer breakdowns that cause the loss of stored data. Due to the cost of data and the damage that its loss entails, hard drive failure prediction is vital. In this context, the objective of this paper is to develop a method for detecting the beginning of hard drive malfunction using streaming SMART data, allowing the user to take actions before the breakdown occurs. This is a challenging task for two main reasons. First, there are not usually many examples of failed hard drives. Second, in these few available examples, hard drives are only identified and labeled as failed after complete breakdown occurs, but the exact moment when they begin to malfunction is usually unknown. Both these aspects significantly complicate the supervised learning of hard drive failure prediction models. To cope with these issues, the problem is addressed as a multidimensional time series streaming classification problem based on sliding windows. Moreover, as a solution to the highly imbalanced situation, the learned classifier is optimized to maximize the minimum recall of classes. Experimental results using the Backblaze benchmark dataset show that the proposed method reliably anticipates hard drive failures and obtains a higher balance between the recall values of both classes, failed and correct disks, compared to other state-of-the-art solutions.
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