Operational Workload Impact on Robust Solid-State Storage Analyzed with Interpretable Machine Learning

Abstract

Solid-state storage technology is finding increasing adoption in enterprise and data center environments due to their high reliability and reducing cost. With high performance solid-state storage devices (SSDs) internally designed as distributed resilient systems, their operational behavior under materially different workloads is described in this research. Application of interpretable machine learning on internal parametric data of SSDs enables insights on workloads' interaction with the resilient system design. After prior research demonstrated significantly different accelerated workload stress, the analysis on resilience of the SSDs under random vs. pseudo-sequential workloads emphasize the efficacy and importance of their distributed resilience schemes. As such, these results provide causational insights on the mechanism of differential stress of the workloads impacting the resilience design principles. Moreover, the results elucidate guidelines strongly relevant from design robustness perspective for research on novel SSD architectures such as the proposed Open Channel SSD, towards deployment in hyperscale and virtualization environments.