A disk failure prediction method based on LSTM network due to its individual specificity
Image credit: BackBlaze
Abstract
In current storage systems, to protect data security, disk failure prediction is required. Machine learning proved to be a method to solve the problem of disk failure prediction. However, because the disk-related values are affected by factors such as their use and usage environment, the values of different disks in the event of a failure are not the same. The normal value on one disk may be the value when another disk fails. Some studies have introduced the concept of time windows into disk failure prediction, trying to improve the ability of disk failure prediction by studying the relationship of a disk’s value change over time, and achieved good prediction results. We chose the neural network with time-series model to further validate the prediction of time series affect performance, and would like to be able to further improve the prediction performance by using a neural network. In this paper, we will introduce a disk failure prediction system based on LSTM networks. Considering the individual differences of the disks, we replace the input in the LSTM network with the continuous running records of the disks. The network will learn the disk information over a period of time and predict whether this disk will fail. With the proposed approach we are able to predict a disk will fail in next fifteen days with an average precision of 86.31. By comparing with other algorithms, our method performs well.
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