FaBSR: a method for cluster failure prediction based on Bayesian serial revision and an application to LANL cluster
Bibliographic record
Abstract
Abstract Accurate failure number prediction of Repairable Large‐scale Long‐running Computing (RLLC) cluster systems is a challenge because of the reparability and large scale of the system. Furthermore, the variational failure rate derived from system maintenance yields a small sample problem, that is, the failure numbers observed from different time phases do not belong to the same population. To address the challenge, a general Bayesian serial revision prediction method (FaBSR) is proposed on the basis of the Time Series and Bootstrap approaches, and it can determine the distribution of failure number, analyze the variation trend of failure rate and accurately predict the failure number. To demonstrate the performance gains of the method, the data of Los Alamos National Laboratory (LANL) cluster system are used as a typical RLLC system to do extensive experiments. And experimental results show that the prediction accuracy of FaBSR is 80.4%, improved by more than 4% compared with other existing methods. Copyright © 2010 John Wiley & Sons, Ltd.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".