Closing the Loop: Building Self-Adaptive Software for Continuous Performance Engineering
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
Cloud computing and cloud-native platforms have rendered runtime environments more malleable. Simultaneously, the growing demand for flexible and agile software applications and services has driven the emergence of self-adaptive architectures. These architectures, in turn, facilitate software performance modeling, tuning, optimization, and scaling in a continuous manner, blurring the boundary between development-time and run-time. Self-adaptive software employs feedback loop controllers inspired by control theory or variations of the Monitoring-Analysis-Planning-Acting (MAPE) architecture. Whether implemented in a centralized or decentralized manner, most controllers utilize performance models that are learned or tuned at run-time. This shift implies that software is designed to be observable and controllable during execution, presupposing the co-design of software applications and their runtime controllers.
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 | 0.001 |
| 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