Synthetic Data Generation for Hard Drive Failure Prediction in Large-scale Systems

Accurate failure prediction is critical for the reliability of HPC facilities and data centers storage systems. This study addresses data scarcity, privacy concerns, and class imbalance in HDD failure datasets by leveraging synthetic data generation. We propose an end-to-end framework to generate synthetic storage data using Generative Adversarial Networks and Diffusion models. We implement a data segmentation approach considering temporal variation of disks access to generate high-fidelity synthetic data that replicates the nuanced temporal and feature-specific patterns of disk failures. Experimental results show that synthetic data achieves similarity scores of 0.81–0.89 and enhances failure prediction performance, with up to 3% improvement in accuracy and 2% in ROC-AUC. With only minor performance drops versus real-data training, synthetically trained models prove viable for predictive maintenance.