Investigations on Disk Performance Indices and Their Statistical Learning for Storage Cluster Disk Fail-Slow Detection
Bibliographic record
Abstract
Disks are critical components of data center storage systems, and their health directly affects system stability. However, when a disk enters a fail-slow state, it continues to function but experiences significant performance degradation, leading to slower response time and negatively affecting business service quality. Being different from disk failure, the fail-slow state is hard to be detected by using traditional disk failure detection methods. The scenario of fail-slow is very complex, therefore a flexible fail-slow processing framework is required, and the detection must be carried out on the premise of ensuring the reliability and high availability of the storage system. To address this challenge, we propose a non-invasive and adaptive method for fail-slow disk detection in storage clusters in this paper. Firstly, a latency prediction model is proposed to intelligently correlate disk performance metrics from a database. Based on this, we propose a health index-based monitoring strategy for fail-slow disk states and cluster health assessment. After deployment in a real production environment, this method has monitored thousands of disks, effectively identifying dozens of fail-slow disks and mitigating business risks caused by hardware issues.
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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 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".