Artificial Intelligence in the Low-Level Realm -- A Survey
It addresses the problem of integrating AI into OS kernels for better performance and trustworthiness, primarily for researchers and developers in resource-constrained systems, but is incremental as it surveys existing work.
This survey reviews efforts and challenges in applying machine learning to improve the core tasks of operating systems in low-resource environments, focusing on how AI can enhance OS kernel functionality beyond user-space applications.
Resource-aware machine learning has been a trending topic in recent years, focusing on making ML computational aspects more exploitable by the edge devices in the Internet of Things. This paper attempts to review a conceptually and practically related area concentrated on efforts and challenges for applying ML in the operating systems' main tasks in a low-resource environment. Artificial Intelligence has been integrated into the operating system with applications such as voice or image recognition. However, this integration is only in user space. Here, we seek methods and efforts that exploit AI approaches, specifically machine learning, in the OSes' primary responsibilities. We provide the improvements that ML can bring to OS to make them more trustworthy. In other words, the main question to be answered is how AI has played/can play a role directly in improving the traditional OS kernel main tasks. Also, the challenges and limitations in the way of this combination are provided.