Leveraging Temporality of Data to Improve Failure Predictions for Solid State Drives in Data Centers
Chandranil Chakraborttii, Jonas Boettner · 2023
This paper introduces an unsupervised multivariate anomaly detection framework leveraging generative adversarial networks (GANs) to predict solid-state drive (SSD) failures. Recent prior research leveraged the spatial locality of drive failures, along with performance data, to improve drive failure predictions. However, this information is seldom available in most publicly available data sets and can be challenging to collect, requiring additional overhead. In this work, we show that drive failures have a strong temporal correlation that can be used to improve drive failure predictions. We analyze drive observations from over 30,000 SSDs from Google’s data center spanning six years and show that our GAN-based approach improves the performance of failed drive predictions by 12% compared to the state-of-the-art. In contrast to prior work, we utilize the difference in drive observations and leverage LSTMs (long short-term memory) with GANs to capture the temporal correlations in data. Additionally, we introduce a hierarchical prediction approach that can accurately predict replacements and infant mortality in SSDs with an accuracy of 79% and 98% respectively.