  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.
