  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%.
