  Physical and cloud storage services are well-served by functioning and
reliable high-volume storage systems. Recent observations point to hard disk
reliability as one of the most pressing reliability issues in data centers
containing massive volumes of storage devices such as HDDs. In this regard,
early detection of impending failure at the disk level aids in reducing system
downtime and reduces operational loss making proactive health monitoring a
priority for AIOps in such settings. In this work, we introduce methods of
extracting meaningful attributes associated with operational failure and of
pre-processing the highly imbalanced health statistics data for subsequent
prediction tasks using data-driven approaches. We use a Bidirectional LSTM with
a multi-day look back period to learn the temporal progression of health
indicators and baseline them against vanilla LSTM and Random Forest models to
come up with several key metrics that establish the usefulness of and
superiority of our model under some tightly defined operational constraints.
For example, using a 15 day look back period, our approach can predict the
occurrence of disk failure with an accuracy of 96.4% considering test data 60
days before failure. This helps to alert operations maintenance well in-advance
about potential mitigation needs. In addition, our model reports a mean
absolute error of 0.12 for predicting failure up to 60 days in advance, placing
it among the state-of-the-art in recent literature.
