Complex Application Architecture Dynamic Reconfiguration Based on Multi-criteria Decision Making
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
Intelligent Transportation Systems (ITS) are increasingly important since they aim to bring solutions to crucial problems related to transportation networks such as congestion and various road incidents. Management of ITS, as other complex and distributed applications, has to cope with unforeseeable events and incomplete data while guaranteeing a quality of service (QoS) defined by multiple criteria reflecting real-life needs. To enable applications to adapt to changing environments, we define a methodology of dynamic architecture reconfiguration based on multi-criteria decision making (MCDM) using evolutionary computing (EC) to find the best combination of architecture components. We use the Pareto Evolutionary Algorithm Adapting the Penalty (PEAP), a category of EC, selected in this paper to deal with timeconsuming online processing required by basic EC such as genetic algorithms. Our simulation results relating to road safety highlight the benefits of MCDM prior to such reconfiguration. We also address the problem of destabilization which can result from repeated reconfigurations in response to ongoing environment changes.
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 | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.002 | 0.000 |
| 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