An extensible framework for repair-driven 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
In recent years autonomic computing, specifically autonomic data centre management has gained significant attention. Human intervention be minimized to reduce the operating costs of business applications. In this paper we focus our attention to the self-repair dimension and present a flexible probabilistic framework to develop agents for self-repair in the context of business-information-system components. Our framework seeks to pick the optimal sequence of repair actions given only imperfect information about the experienced fault. In contrast to existing recovery-oriented approaches, our model explicitly considers fault prevalence, symptoms of recurrent failures, and inclusive repair actions. We evaluate our proposal using discrete event simulation. Our evaluation shows that an optimal repair policy can be computed from a brief specification of repair actions. Even in the context of very unreliable error detection our controller is able to estimate the current state of the monitored system and recover from failure.
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 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| 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