Kernel-Level Event-Based Performance Anomaly Detection in Software Systems under Varying Load Conditions
Bibliographic record
Abstract
Performance anomalies in software systems can lead to significant disruptions and reduced user satisfaction. Traditional methods of anomaly detection rely on log events that capture higher-level system activities but may lack the details to effectively pinpoint root causes. This study investigates the detection of performance anomalies in software systems using kernel-level event data. By leveraging both classical and deep learning approaches, we developed models capable of identifying anomalous patterns in system behavior. The experimental dataset, consisting of over 24 million events collected under various noise and workload conditions, provided a comprehensive basis for analysis. Our results show the robustness of ensemble techniques in predicting performance anomalies with the random forest (accuracy = 89%) and ensemble stacking (F1 score= 0.76, AUC= 0.84) models outperforming other classifiers. Feature importance analysis revealed that CPU-bound events, such as sched_switch and sched_wakeup, are key indicators of performance anomalies. Additionally, a significant relationship was identified between system workload conditions and the likelihood of anomalies, as confirmed by statistical testing. These findings highlight the potential of kernel-level data for precise anomaly detection and provide insights for optimizing system monitoring and performance management.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 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".