Abstract
PREDICTIVE MODEL OF CPU-BURST TIME IN COMPUTATIONAL GRIDS: A COMPARATIVE STUDY OF MACHINE LEARNING APPROACHES
Sudarshan M. G., Vinutha M. R., P. S. Amrutha Lakshmi and Nischith Gowda D. Y.*
ABSTRACT
Analysing different Machine Learning approaches to predict bursttimes in CPU. This study explores the proposal of machine learningalgorithms, entailing Random Forest, Gradient Boosting, DecisionTree, and K-Nearest Neighbors (KNN), to predict CPU burst times incompute grids. The Auver Grid dataset, encompassing diverse jobattributes such as Job ID, Submit Time, Wait Time, Run Time, N Procs, and Req Memory,was utilized for model training and evaluation.
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