Resource Life-Cycle Aware Noise Detection via Kernel Event Monitoring
Bibliographic record
Abstract
Diverse noises significantly impact process performance, posing a challenge for efficient and reliable system management. Various noise sources with distinct root causes demand precise analysis for effective mitigation. However, noise detection methods must operate non-intrusively to avoid masking the very noise they aim to identify. This paper introduces a novel approach leveraging kernel-level event monitoring for noise detection and root cause analysis. Our method passively collects kernel events without disrupting system execution and calculates metrics aligned with the request life-cycle of critical resources (CPU, disk, network) to monitor noise throughout execution. As kernel events correspond to each phase of the request's life-cycle, our approach offers a comprehensive overview of system noises. Moreover, the detailed information provided by kernel events enables our approach to precisely identify the specific phases responsible for noise generation. Additionally, we introduce visualization tools to aid administrators in identifying patterns, anomalies, and root causes, thereby facilitating informed decision-making and real-time monitoring. Experimental evaluations across diverse test cases validate the efficiency and accuracy of our approach in monitoring various resource request life-cycles and precisely detecting noises through kernel-level event analysis. This research contributes a valuable tool for enhancing system performance and reliability by enabling proactive and targeted noise mitigation strategies.
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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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 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".