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Reiner-Benaim, Anat; Grabarnick, Anna; Shmueli, Edi (2016)
Publisher: The Science and Information (SAI) Organization
Journal: International Journal of Advanced Research in Artificial Intelligence
Languages: English
Types: Article
Subjects: Runtime Prediction, Statistics - Machine Learning, Job Scheduler, Electronic computers. Computer science, Classifier, Server Farms, Mixture Distribution, QA75.5-76.95
Separating the short jobs from the long is a known technique to improve scheduling performance. In this paper we describe a method we developed for accurately predicting the runtimes classes of the jobs to enable this separation. Our method uses the fact that the runtimes can be represented as a mixture of overlapping Gaussian distributions, in order to train a CART classifier to provide the prediction. The threshold that separates the short jobs from the long jobs is determined during the evaluation of the classifier to maximize prediction accuracy. Our results indicate overall accuracy of 90% for the data set used in our study, with sensitivity and specificity both above 90%.
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