Application of Deep Learning on Integrating Prediction, Provenance, and Optimization
Pacific Northwest National Laboratory - Richland,
2 University of California - San Diego, CA, USA
* e-mail: email@example.com
Published online: 17 September 2019
In this research, we investigated two approaches to detect job anomalies and/or contention for large scale computing efforts: 1. Preemptive job scheduling using binomial classification long short-term memory networks 2. Forecasting intra-node computing loads from the active jobs and additional job(s) For approach 1, we achieved a 14% improvement in computational resources utilization and an overall classification accuracy of 85% on real tasks executed in a High Energy Physics computing workflow. For this paper, we present the preliminary results used in second approach.
© The Authors, published by EDP Sciences, 2019
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