Online Job Failure Prediction in an HPC System
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Abstract
Modern High Performance Computing (HPC) systems are complex machines, with major impacts on economy and society. Along with their computational capability, their energy consumption is also steadily raising, representing a critical issue given the ongoing environmental and energetic crisis. Therefore, developing strategies to optimize HPC system management has paramount importance, both to guarantee top-tier performance and to improve energy efficiency. One strategy is to act at the workload level and highlight the jobs that are most likely to fail, prior to their execution on the system. Jobs failing during their execution unnecessarily occupy resources which could delay other jobs, adversely affecting the system performance and energy consumption. In this paper, we study job failure prediction at submit-time using classical machine learning algorithms. Our novelty lies in (i) the combination of these algorithms with Natural Language Processing (NLP) tools to represent jobs and (ii) the design of the approach to work in an online fashion in a real system. The study is based on a dataset extracted from a production machine hosted at the HPC centre CINECA in Italy. Experimental results show that our approach is promising.
