Multi-Instance Adversarial Learning Domain Adaptation Network for Failure Prediction of Unlabeled Solid-State Drives

Recently, with the large-scale application of solid-state drives (SSDs), the failure of SSDs has become the main reliability issue in data centers. SSD manufacturers developed self-monitoring, analysis, and reporting technology (SMART) to indicate the health status of SSDs. However, for the newly enabled and new types of SSDs in the data centers, lack of enough labeled data will be a critical problem, making failure prediction for SSDs a difficult task. To this end, this article proposes a multi-instance adversarial learning domain adaptation network (MALDAN) for coping with this task. A multi-instance learning method with an attention mechanism is designed to solve the problem of features extraction from unlabeled data by assigning weights to features over the lifespan. Moreover, the distribution differences between different SSD models prevent the knowledge of the labeled information from being used for failure prediction of the unlabeled data, and an adversarial domain adaptation (DA) method is used to align the distributions. Finally, the proposed method is verified on Alibaba's dataset and shows much better performance than other methods.