Learning to Drive Software-Defined Solid-State Drives

Thanks to the mature manufacturing techniques, flash-based solid-state drives (SSDs) are highly customizable for applications today, which brings opportunities to further improve their storage performance and resource utilization. However, the SSD efficiency is usually determined by many hardware parameters, making it hard for developers to manually tune them and determine the optimized SSD hardware configurations. In this paper, we present an automated learning-based SSD hardware configuration framework, named AutoBlox, that utilizes both supervised and unsupervised machine learning (ML) techniques to drive the tuning of hardware configurations for SSDs. AutoBlox automatically extracts the unique access patterns of a new workload using its block I/O traces, maps the workload to previous workloads for utilizing the learned experiences, and recommends an optimized SSD configuration based on the validated storage performance. AutoBlox accelerates the development of new SSD devices by automating the hardware parameter configurations and reducing the manual efforts. We develop AutoBlox with simple yet effective learning algorithms that can run efficiently on multi-core CPUs. Given a target storage workload, our evaluation shows that AutoBlox can deliver an optimized SSD configuration that can improve the performance of the target workload by 1.30 × on average, compared to commodity SSDs, while satisfying specified constraints such as SSD capacity, device interfaces, and power budget. And this configuration will maximize the performance improvement for both target workloads and non-target workloads.