AIOPS Prediction for Hard Drive Failures Based on Stacking Ensemble Model
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
This paper mainly shares a successful Artificial Intelligence for IT Operations (AIOps) solution we have built for a cloud storage array to deal with unbalanced hard disk failure data and predict disk failure. Firstly, we preprocessed the unbalanced disk data to filter out irrelevant raw data. Based on SMART (Self-Monitoring, Analysis, and Reporting Technology) attributes, we extracted 14 preliminary attributes as training features for disk failure prediction. Secondly, we used a feature extraction library called Tsfresh and 16 extraction methods to regenerate more than 1500 features. To accelerate machine learning process, we used the Benjamini Yekutieli procedure with a significance test to select the most relevant features. Since a single predictive model no longer performs sufficiently well on the unbalanced dataset, we finally input the prediction results calculated by three algorithms(XGBoost classification, LSTM classification, and XGBoost regression) as new features input of a stacking ensemble learning model that can generate more stable and accurate prediction results. The experimental results showed that the proposed stacking ensemble learning model can accurately predict the disk failure and necessity of disk replacement 0 to 14 days, 14 to 42 days and more days in advance.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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