PreF: Predicting job failure on supercomputers with job path and user behavior

Abstract

Large numbers of jobs are executed on supercomputers almost every day. Unfortunately, many jobs would fail for various reasons, resulting in the waste of resources and the prolonged waiting time for queuing jobs. Job failure prediction can guide adjustment measures in advance, which is vital to the system's overall execution efficiency and reliability. Aiming at the problem that the existing job failure prediction methods are single, the collection of job features is complex and challenging to apply. This article strives to study whether these failed jobs can be predicted with known and synthetic features. We perform a comprehensive analysis of large amounts of historical data and various features and find that two novel features (running path and retry count) can predict job failure well. The running path indicates the application type a job belongs to, and the retry count reflects the user's behavior when the job fails. We propose a job failure prediction framework called PreF on supercomputers using machine learning based on the novel features. The experimental results show that PreF can correctly identify over 89% of jobs, outperforming the latest related methods on the comprehensive evaluation indicator (S_score) by around 4%.

FP‐JSC: Job failure prediction on supercomputers through job application sequence correlation

“…In the studies by Banjongkan et al 19 and Yoo et al, 20 although they employ tree structure models, their algorithms are single, and they do not consider the correlation of job application sequences as addressed in this article, leading to weaker predictive performance. Furthermore, compared to our previous research work, 21,22 the FP-JSC framework has achieved a promising prediction effect.…”

“…In this article, we are attempting to construct a machine learning model with rapid computing efficiency, robust interpretability, and no prior knowledge assumptions. In our previous research, 21,22 it was demonstrated that tree structure models' learning algorithms are better suited for our HPC system. Hence, we have opted for the following three learning algorithms.…”