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Research Article | Open Access |

Highly Accurate Prediction of Jobs Runtime Classes

Author 1: Anat Reiner-Benaim Author 2: Anna Grabarnick Author 3: Edi Shmueli
International Journal of Advanced Research in Artificial Intelligence (IJARAI) · Vol. 5, No. 6 · Published 2016

DOI: 10.14569/IJARAI.2016.050606

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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%.

Keywords

Runtime Prediction Job Scheduler Server Farms Classifier Mixture Distribution

How to Cite this Article

Reiner-Benaim, A., Grabarnick, A., & Shmueli, E. (2016). Highly Accurate Prediction of Jobs Runtime Classes. International Journal of Advanced Research in Artificial Intelligence, 5(6). https://doi.org/10.14569/IJARAI.2016.050606

Reiner-Benaim, Anat, et al.. "Highly Accurate Prediction of Jobs Runtime Classes." International Journal of Advanced Research in Artificial Intelligence, vol. 5, no. 6, 2016, https://doi.org/10.14569/IJARAI.2016.050606.

@article{Reiner-Benaim2016,
  title     = {Highly Accurate Prediction of Jobs Runtime Classes},
  journal   = {International Journal of Advanced Research in Artificial Intelligence},
  volume    = {5},
  number    = {6},
  year      = {2016},
  publisher = {The Science and Information Organization},
  author    = {Anat Reiner-Benaim and Anna Grabarnick and Edi Shmueli},
  doi       = {10.14569/IJARAI.2016.050606},
  url       = {https://doi.org/10.14569/IJARAI.2016.050606}
}

Open Access — licensed under a Creative Commons Attribution 4.0 International License. Unrestricted use, distribution, and reproduction in any medium, even commercially, as long as the original work is properly cited.

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