Complex Application Architecture Dynamic Reconfiguration Based on Multi-criteria Decision Making
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.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".