Improving context-awareness in self-adaptation using the DYNAMICO reference model
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
Self-adaptation mechanisms modify target systems dynamically to address adaptation goals, which may evolve continuously due to changes in system requirements. These changes affect values and thresholds of observed context variables and monitoring logic, or imply the addition and/or deletion of context variables, thus compromising self-adaptivity effectiveness under static monitoring infrastructures. Nevertheless, self-adaptation approaches often focus on adapting target systems only rather than monitoring infrastructures. Previously, we proposed DYNAMICO, a reference model for self-adaptive systems where adaptation goals and monitoring requirements change dynamically. This paper presents an implementation of DYNAMICO comprising our SMARTERCONTEXT monitoring infrastructure and QoS-CARE adaptation framework in a self-adaptation solution that maintains its context-awareness relevance. To evaluate our reference model we use self-adaptive system properties and the Znn.com exemplar to compare the Rainbow system with our DYNAMICO implementation. The results of the evaluation demonstrate the applicability, feasibility, and effectiveness of DYNAMICO, especially for self-adaptive systems with context-awareness requirements. © 2013 IEEE.
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 | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.004 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.000 | 0.001 |
| 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