Collaborative Data Completing for Robust SSD Failure Prediction

Solid-state drives (SSDs) are integral to modern data centers due to their superior performance and efficiency. However, ensuring their reliability under large-scale deployment remains challenging, with failures exhibiting widespread and diverse patterns across drive models and failure types. Factors such as mislabeling, missing data, and class imbalance significantly hinder accurate predictions and can obscure failure-relevant signals. While advanced machine learning models, such as GANs and CNN-LSTM architectures, have shown promise, they often struggle to address these data quality issues effectively. We propose an integrated data preprocessing framework for SSD failure prediction that combines feature screening, fault-aware imputation, and model-wise class balancing. The framework is designed to address heterogeneous failure types, structured missingness, and severe class imbalance under a leakage-free evaluation protocol. The integrated effect of the three preprocessing steps achieves significant improvement on the prediction performance. Testing on a dataset of 700,000 NVMe SSDs from Alibaba, our approach improves recall from 51% to 73% and precision from 59% to 88% under a leakage-free train and test split, outperforming representative state-of-the-art pipelines. These results demonstrate the effectiveness of tailoring preprocessing methods to the unique challenges of SSD failure prediction, enhancing reliability in real-world operations.