  To provide proactive fault tolerance for modern cloud data centers, extensive
studies have proposed machine learning (ML) approaches to predict imminent disk
failures for early remedy and evaluated their approaches directly on public
datasets (e.g., Backblaze SMART logs). However, in real-world production
environments, the data quality is imperfect (e.g., inaccurate labeling, missing
data samples, and complex failure types), thereby degrading the prediction
accuracy. We present RODMAN, a robust data preprocessing pipeline that refines
data samples before feeding them into ML models. We start with a large-scale
trace-driven study of over three million disks from Alibaba Cloud's data
centers, and motivate the practical challenges in ML-based disk failure
prediction. We then design RODMAN with three data preprocessing echniques,
namely failure-type filtering, spline-based data filling, and automated
pre-failure backtracking, that are applicable for general ML models. Evaluation
on both the Alibaba and Backblaze datasets shows that RODMAN improves the
prediction accuracy compared to without data preprocessing under various
settings.
