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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%.

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