Autonomic Computing Now You See It, Now You Don't Design and Evolution of Autonomic Software Systems
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
With the rapid growth of web services and socio-technical ecosystems, the management complexity of these modern, decentralized, distributed computing systems presents significant challenges for busi- nesses and often exceeds the capabilities of human operators. Autonomic computing is an effective set of technologies, models, architecture pat- terns, standards, and processes to cope with and reign in the manage- ment complexity of dynamic computing systems using feedback control, adaptation, and self-management. At the core of an autonomic system are control loops which sense their environment, model their behavior in that environment, and take action to change the environment or their own behavior. Computer science researchers often approach the design of such highly dynamical systems from a software architecture perspective whereas engineering researchers start with a feedback control perspec- tive. In this article, we argue that both design perspectives are needed and necessary for autonomic system design.
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 | 0.001 | 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