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Flash-based solid state drives represent an important storage tier in today's hyperscale data centers. Although solid state drives (SSDs) are relatively reliable, data center operators are interested in predicting future drive failures to administer drive replacement, data migration, and drive acquisition strategies. We analyze telemetry data from over 30,000 SSDs running live applications in Google's datacenters over a span of six years, for predicting and explaining SSD failures using machine learning techniques. We propose the use of 1-class isolation forest and autoencoder-based anomaly detection techniques for predicting previously unseen SSD failure types with high accuracy. We show that ignoring the minority class for training can improve the performance by up to 9.5% and if adaptability to dynamic environments is required, by up to 13%. Furthermore, this paper proposes to utilize 1-class autoencoders to enable model interpretability. In particular, our autoencoder-based approach enables reasoning about the causes that lead to SSD failures. Common to all approaches, we deploy a set of powerful feature selection techniques that improve the model performance by up to 1.3× and reduce training times by up to 1.8×.

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Flash-based solid state drives lack support for in-place updates, and hence deploy a flash translation layer to absorb the writes. For this purpose, SSDs implement a log-structured storage system introducing garbage collection and writeamplification overheads. In this paper, we present a machine learning based approach for reducing write amplification in log structured file systems via death-time prediction of logical block addresses. We define death-time of a data element as the number of I/O writes before which the data element is overwritten. We leverage the sequential nature of I/O accesses to train lightweight, yet powerful, temporal convolutional network (TCN) based models to predict deathtimes of logical blocks in SSDs. We leverage the predicted death-times in designing ML-DT , a near-optimal data placement technique that minimizes write amplification (WA) in log structured storage systems. We compare our approach with three state-of-the-art data placement schemes and show that ML-DT achieves the lowest WA by utilizing the learnt I/O death-time patterns from real-world storage workloads. Our proposed approach results in up to 14% reduction in write amplification compared to the best baseline technique. Additionally, we present a mapping learning technique to test the applicability of our approach to new or unseen workloads and present a hyper-parameter sensitive study.

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