Storage systems have evolved over decades into a complicated ecosystem that includes storage hardware, system software, and data applications. This growing complexity poses significant challenges for storage development and deployment. However, current human-driven, heuristic-based approaches to building storage systems cannot rapidly meet the ever-increasing storage performance and efficiency demands of applications. As we embrace the advancement of machine learning (ML) today, it is now the golden age to invent new approaches to develop and deploy storage systems with ML techniques.

In this talk, I will present my research on building a learning-based storage ecosystem. Specifically, I will demonstrate how we can explore ML to (1) automate and accelerate storage hardware development, (2) optimize system software for improved memory efficiency and storage performance, and (3) manage the complexity of cloud storage deployment at scale. I will also discuss how my work sheds light on the development of “ML for systems” in general.