Towards Generic Failure-Prediction Models in Large-Scale Distributed Computing Systems
Bibliographic record
Abstract
The increasing complexity of Distributed Computing (DC) systems requires advanced failure-prediction models to enhance reliability and efficiency. This study proposes a comprehensive methodology for developing generic machine learning (ML) models capable of cross-layer and cross-platform failure-prediction without requiring platform-specific retraining. Using the Grid5000 failure dataset from the Failure Trace Archive (FTA), we explored Linear and Logistic Regression, Random Forest, and XGBoost to predict three critical metrics: Time Between Failures (TBF), Time to Return/Repair (TTR), and Failing Node Identification (FNI). Our approach involved extensive exploratory data analysis (EDA), statistical examination of failure patterns, and model evaluation across the cluster, site, and system levels. The results demonstrate that XGBoost consistently outperforms the other models, achieving near-perfect 100% accuracy for TBF and FNI, with robust generalisability across diverse DC environments. In addition, we introduce a hierarchical DC architecture that integrates these failure-prediction models. In the form of a use case, we also demonstrate how service providers can use these prediction models to balance service reliability and cost.
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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 | 0.005 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.000 | 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".