Modern machine-learning and artificial intelligence problems are not (only) algorithmetic ones but (also) systems ones. We are seeking solutions for both building better systems for ML/AI and using ML/AI to build systems.
Using ML to Build OS
With operating systems being at the core of computer systems, decades of research and engineering efforts have been put into the development of OSes. To keep pace with the speed of modern hardware and application evolvement, we argue that a different approach should be taken in future OS development. Instead of relying solely on human wisdom, we should also leverage AI and machine learning techniques to automatically “learn” how to build and tune an OS. This paper explores the opportunities and challenges of the “learned” OS approach and makes recommendation for future researchers and practitioners on building such an OS.
Conferences and Journals
"Learned" Operating Systems
Yiying Zhang, Yutong Huang
ACM SIGOPS Operating Systems Review 53(1): 40-45 (2019)