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The operating system's role in a computer system is to manage the variousresources. One of these resources is the Central Processing Unit. It is managedby a component of the operating system called the CPU scheduler. Schedulers areoptimized for typical workloads expected to run on the platform. However, asingle scheduler may not be appropriate for all workloads. That is, a schedulermay schedule a workload such that the completion time is minimized, but whenanother type of workload is run on the platform, scheduling and thereforecompletion time will not be optimal; a different scheduling algorithm, or adifferent set of parameters, may work better. Several approaches to solvingthis problem have been proposed. The objective of this survey is to summarizethe approaches based on data mining, which are available in the literature. Inaddition to solutions that can be directly utilized for solving this problem,we are interested in data mining research in related areas that have potentialfor use in operating system scheduling. We also explain general technicalissues involved in scheduling in modern computers, including parallelscheduling issues related to multi-core CPUs. We propose a taxonomy thatclassifies the scheduling approaches we discuss into different categories.

Use of Data Mining in Scheduler Optimization