System Monitoring with Metric-Correlation Models
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
Modern software systems expose management metrics to help track their health. Recently, it was demonstrated that correlations among these metrics allow errors to be detected and their causes localized. Prior research shows that linear models can capture many of these correlations. However, our research shows that several factors may prevent linear models from accurately describing correlations, even if the underlying relationship is linear. Common phenomena we have observed include relationships that evolve, relationships with missing variables, and heterogeneous residual variance of the correlated metrics. Usually these phenomena can be discovered by testing for heteroscedasticity of the underlying linear models. Such behaviour violates the assumptions of simple linear regression, which thus fail to describe system dynamics correctly. In this paper we address the above challenges by employing efficient variants of Ordinary Least Squares regression models. In addition, we automate the process of error detection by introducing the Wilcoxon Rank-Sum test after proper correlations modeling. We validate our models using a realistic Java-Enterprise-Edition application. Using fault-injection experiments we show that our improved models capture system behavior accurately.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| 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