SmartOS: Towards Automated Learning and User-Adaptive Resource Allocation in Operating Systems

Today's operating systems typically apply a one-size-fits-all approach to resource management, such as applying a scheduler that treats all processes of equal importance. The goal of this paper is to explore a learning-based approach to resource management in modern operating systems in which the OS automatically learns what tasks the user deems to be most important at that time and seamlessly adjusts allocation of CPU, memory, I/O, and network bandwidth to prioritize user preferences on demand. We demonstrate an implementation of such a learning-based OS in Linux and present evaluation results showing that a reinforcement learning-based approach can rapidly learn and adjust system resources to meet user demands.

Automated summary

This paper explores a learning-based approach to resource management in modern operating systems, where the OS can automatically prioritize user preferences by learning what tasks are most important at any given time. The authors demonstrate an implementation of this learning-based OS in Linux and show through evaluation results that a reinforcement learning-based approach can effectively adapt system resources to meet user demands.