Machine Learning for Predicting Infrastructure Faults and Job Failures in Clouds: A Survey
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Fault prediction in cloud environments is a critical task for ensuring the reliability and availability of cloud services. Machine learning (ML) techniques are increasingly used for this purpose due to their ability to recognize and predict patterns that may indicate potential faults. In this survey, we propose a taxonomy for ML-based fault prediction work, providing a critical overview, and evaluating the work from an algorithmic perspective. This includes identifying five key requirements for fault prediction in clouds and using them to evaluate the work. In this evaluation, we gain insight into the literature's current state and identify research directions. Our evaluations indicate that adaptability and interpretability requirements are not adequately met in the current literature, and future research could focus on developing fault prediction solutions that are adaptable to dynamic cloud environments, and can provide interpreta-ble explanations for their predictions.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 it