Using ML in Designing Self-healing OS
Maqsood Ahmad, Muhammad Samiullah, Muhammad Jawad
Journal of Information Organization, Vol. 6, No. 2, 2016.
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Abstract:
Operating systems serve as executing platforms and resource manager and supervisors for the applications in running phase. With the development of more complex computer systems and applications, the required operating systems become complex too. But the proper management of such complex operating systems by human beings has shown to be impractical. Nowadays, self-managing concepts provide the basis for developing appropriate mechanisms to handle complex systems with minimum human interventions. Although the implications of deploying self-managing and autonomic attributes and concepts at the application levels have been studied, their deployment at system software level such as in operating systems have not been fully studied. Self-managed applications may not enjoy the whole benefit of self-management if the platform on which they run, specially its operating system, is not self-managed.  Given this requirement, this paper highlights the most frequently occurred faults and anomalies of operating systems, and proposes a tiered operating system architecture and model, and a corresponding self-healing mechanism using machine learning techniques to show how  self-managing can be realized at operating system level. Based on the principles of autonomic computing and self-adapting system research, we identify self-healing systemsâ€™ fundamental principles. The main objective has been to design the operating system resilient to operating system faults without restarting the operating system and less human interaction.