System performance anomaly detection using tracing data analysis
Bibliographic record
Abstract
In recent years, distributed systems have become increasingly complex as they grow in both scale and functionality. Such complexity makes these systems prone to performance anomalies. Efficient anomaly detection frameworks enable rapid recovery mechanisms to increase the system's reliability. In this paper, we present an anomaly detection approach for practical monitoring of processes running on a system to detect anomalous vectors of system calls. Our proposed methodology employs a Linux tracing toolkit (LTTng) to monitor the processes running on a system and extracts the streams of system calls. The system calls streams are split into short sequences using a sliding window strategy. Unlike previous studies, our proposed approach computes the execution time of system calls in addition to the frequency of each individual call in a window. Finally, a multi-class support vector machine approach is applied to evaluate the performance of the system and detect the anomalous sequences. A comprehensive experimental study on a real dataset collected using LTTng demonstrates that our proposed method is able to distinguish normal sequences from anomalous ones with CPU or memory related problems.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.007 | 0.004 |
| 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".