An Evaluation of Entropy Based Approaches to Alert Detection in High Performance Cluster Logs
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Manual alert detection on modern high performance clusters (HPC) is cumbersome given their increasing complexity and size of their logs. The ability to automatically detect such alerts quickly and accurately with little or no human intervention is therefore desirable. The entropy-based approach of the Nodeinfo framework, which is in production use at Sandia National Laboratories, is one approach to automatic alert detection in HPC logs. In this work, we perform a comparative evaluation of three entropy based techniques, which are modifications to Nodeinfo. We evaluate these systems using three performance metrics, namely (i) Computational cost, (ii) detection accuracy, and (iii) false positive rate. Our results show that there is still room for improvement in entropy based approaches to the task of alert detection. We also show experimentally that it is possible to detect 100% of all alerts while maintaining an effective false positive rate of 0% using an entropy based approach. Our work suggests that entropy based approaches are viable for automatic alert detection in HPC and can improve the dependability of such systems if applied.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 it