Monitoring multi-tier clustered systems with invariant metric relationships
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
To ensure high availability, self-managing systems require self-monitoring and a system model against which to ana-lyze monitoring data. Characterizing relationships between system metrics has been shown to model simple multi-tier transaction systems effectively, enabling failure detection and fault diagnosis. In this paper we show how to extend this invariant metric-relationships approach to clustered multi-tier systems. We show through analysis and experimenta-tion that näıve application of the approach increases cost dramatically while reducing diagnosis accuracy. We demon-strate that randomization at the load balancer during the invariant-identification phase will improve diagnosis accu-racy, though it neither completely eliminates the problem nor reduces the cost; indeed, it may increase the cost, as this approach will require a long learning phase to remove all accidental correlations. Finally, we argue that knowing the system structure is necessary to effectively apply invari-ants to the clustered environment.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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