Artificial Intelligence in the Low-Level Realm -- A Survey

TL;DR

This survey explores how machine learning can be directly integrated into operating system kernels to enhance their functionality and trustworthiness in low-resource environments, highlighting current efforts, challenges, and future directions.

Contribution

It reviews existing approaches and discusses the potential and challenges of applying AI techniques directly within OS kernels, a less explored area compared to user-space AI applications.

Findings

AI can improve OS kernel trustworthiness and efficiency

Current efforts mainly focus on user-space AI applications

Challenges include resource constraints and integration complexity

Abstract

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.