AIOPS Prediction for Hard Drive Failures Based on Stacking Ensemble Model
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.
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.
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 | 0.001 | 0.002 |
| 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 | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".