Learning from Failure Across Multiple Clusters: A Trace-Driven Approach to Understanding, Predicting, and Mitigating Job Terminations
Bibliographic record
Abstract
In large-scale computing platforms, jobs are prone to interruptions and premature terminations, limiting their usability and leading to significant waste in cluster resources. In this paper, we tackle this problem in three steps. First, we provide a comprehensive study based on log data from multiple large-scale production systems to identify patterns in the behaviour of unsuccessful jobs across different clusters and investigate possible root causes behind job termination. Our results reveal several interesting properties that distinguish unsuccessful jobs from others, particularly w.r.t. resource consumption patterns and job configuration settings. Secondly, we design a machine learning-based framework for predicting job and task terminations. We show that job failures can be predicted relatively early with high precision and recall, and also identify attributes that have strong predictive power of job failure. Finally, we demonstrate in a concrete use case how our prediction framework can be used to mitigate the effect of unsuccessful execution using an effective task-cloning policy that we propose.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".