Selection of CPU scheduling dynamically through machine learning
Sara Tehsin, Yame Asfia, Naeem Akbar, Farhan Riaz, Saad Rehman, Rupert Young
Abstract
CPU performance is estimated from the execution of processes per unit time. The selection of the CPU scheduling algorithm in less time is a vital issue. In this paper, a novel approach has been proposed in which selection of an appropriate CPU scheduling algorithm is done through machine learning algorithms dynamically. The result of the proposed algorithm is shown in the experimental section. Through experimentation, it is found that a decision tree gives better results in terms of accuracy and computational time as compared to other machine learning algorithms.
Topics & Concepts
Computer scienceScheduling (production processes)Selection (genetic algorithm)Parallel computingOperating systemArtificial intelligenceEngineeringOperations managementScheduling and Optimization Algorithms