Bibliographic record
Abstract
One aspect of autonomic computing is the ability to identify, separate and automatically tune parameters related to performance, security, robustness and other properties of a software system. Often the response to events affecting these properties consists of adjusting tuneable system parameters such as table sizes, timeout limits, restart checks and so on. In many ways these tuneable parameters correspond to the switches and potentiometers on the control panel of many hardware devices. While modern software systems designed for autonomic control may make these parameters easily accessible, in legacy systems they are often scattered or deeply hidden in the software source.In this paper we introduce Software Tuning Panels for Autonomic Control (STAC), a system for automatically re-architecting legacy software systems to facilitate autonomic control. STAC works to isolate tuneable system parameters into one visible area of a system, producing a resulting architecture that can be used in conjunction with an autonomic controller for self-maintenance and tuning. A proof-of-concept implementation of STAC using source transformation is presented along with its application to the automatic re-architecting of two open source Java programs. Use of the new architecture in monitoring and autonomic control is demonstrated on these examples.
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.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.136 | 0.114 |
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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".