Highly Accurate Prediction of Jobs Runtime Classes
Journal Title: International Journal of Advanced Research in Artificial Intelligence(IJARAI) - Year 2016, Vol 5, Issue 6
Abstract
Separating the short jobs from the long is a known technique to improve scheduling performance. This paper describes a method developed for accurately predicting the runtimes classes of the jobs to enable the 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. The results indicate overall accuracy of 90% for the data set used in the study, with sensitivity and specificity both above 90%.
Authors and Affiliations
Anat Reiner-Benaim, Anna Grabarnick, Edi Shmueli
Keywords
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- EP ID EP149230
- DOI 10.14569/IJARAI.2016.050606
- Views 94
- Downloads 0
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