Filtering System Metrics for Minimal Correlation-Based Self-Monitoring
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
Self-adaptive and self-organizing systems must be self-monitoring. Recent research has shown that self-monitoring can be enabled by using correlations between monitoring variables (metrics). However, computer systems often make a very large number of metrics available for collection. Collecting them all not only reduces system performance, but also creates other overheads related to communication, storage, and processing. In order to control the overhead, it is necessary to limit collection to a subset of the available metrics. Manual selection of metrics requires a good understanding of system internals, which can be difficult given the size and complexity of modern computer systems. In this paper, assuming no knowledge of metric semantics or importance and no advance availability of fault data, we investigate automated methods for selecting a subset of available metrics in the context of correlation-based monitoring. Our goal is to collect fewer metrics while maintaining the ability to detect errors. We propose several metric selection methods that require no information beside correlations. We compare these methods on the basis of fault coverage. We show that our minimum spanning tree-based selection performs best, detecting on average 66% of faults detectable by full monitoring (i.e., using all considered metrics) with only 30% of the metrics.
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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 | 0.000 | 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 | 0.000 |
| Open science | 0.001 | 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