Abstract
In modern data centers, storage system failures are major contributors to downtimes and maintenance costs. Predicting these failures by collecting measurements from disks and analyzing them with machine learning techniques can effectively reduce their impact, enabling timely maintenance. While there is a vast literature on this subject, most approaches attempt to predict hard disk failures using either classic machine learning solutions, such as Random Forests (RFs) or deep Recurrent Neural Networks (RNNs). In this work, we address hard disk failure prediction using Temporal Convolutional Networks (TCNs), a novel type of deep neural network for time series analysis. Using a real-world dataset, we show that TCNs outperform both RFs and RNNs. Specifically, we can improve the Fault Detection Rate (FDR) of \(\approx \)7.5% (FDR = 89.1%) compared to the state-of-the-art, while simultaneously reducing the False Alarm Rate (FAR = 0.052%). Moreover, we explore the network architecture design space showing that TCNs are consistently superior to RNNs for a given model size and complexity and that even relatively small TCNs can reach satisfactory performance. All the codes to reproduce the results presented in this paper are available at https://github.com/ABurrello/tcn-hard-disk-failure-prediction.
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Notes
- 1.
For this input window size, the TCN achieves a FDR similar to the RF, but the following experiments show that the TCN FDR improves for longer inputs.
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This work has been partially supported by the H2020 project IoTwins (g.a. 857191).
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Burrello, A., Pagliari, D.J., Bartolini, A., Benini, L., Macii, E., Poncino, M. (2021). Predicting Hard Disk Failures in Data Centers Using Temporal Convolutional Neural Networks. In: Balis, B., et al. Euro-Par 2020: Parallel Processing Workshops. Euro-Par 2020. Lecture Notes in Computer Science(), vol 12480. Springer, Cham. https://doi.org/10.1007/978-3-030-71593-9_22
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