Selection of CPU scheduling dynamically through machine learning

Sara Tehsin; Yame Asfia; Naeem Akbar; Farhan Riaz; Saad Rehman; Rupert Young

doi:10.1117/12.2559540

22 April 2020 Selection of CPU scheduling dynamically through machine learning

Sara Tehsin, Yame Asfia, Naeem Akbar, Farhan Riaz, Saad Rehman, Rupert Young

Proceedings Volume 11400, Pattern Recognition and Tracking XXXI; 114000O (2020) https://doi.org/10.1117/12.2559540
Event: SPIE Defense + Commercial Sensing, 2020, Online Only

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.

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Sara Tehsin, Yame Asfia, Naeem Akbar, Farhan Riaz, Saad Rehman, and Rupert Young "Selection of CPU scheduling dynamically through machine learning", Proc. SPIE 11400, Pattern Recognition and Tracking XXXI, 114000O (22 April 2020); https://doi.org/10.1117/12.2559540

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