Workshop: Machine Learning for Systems

Learning to Drive Software-Defined Storage

Jian Huang · Daixuan Li · Jinghan Sun


Thanks to the development of manufacturing technology, storage devices suchas solid-state drives (SSDs) are becoming highly customizable to meet the ever-increasing demands on storage performance and capacity for different applications(i.e., software-defined storage). However, it is challenging to develop optimizedstorage devices with current human-driven systems-building approaches, due tothe complicated storage stack. In this paper, we present learning-based approachesto facilitating the development of software-defined storage. To accelerate themanufacturing of efficient storage devices, we enable the automated learning ofoptimized hardware specifications for developing customized storage devices forspecific application types. Our preliminary study shows that utilizing learning-based techniques to drive the development of software-defined storage is promising.

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