Routing and Wavelength Assignment for Prioritized Demands Under a Scheduled Traffic Model
Why this work is in the frame
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Bibliographic record
Abstract
In the scheduled traffic model, the design problem is to allocate resources to a set of demands whose setup and teardown times are known in advance. A number of integer linear program (ILP) solutions for this problem have been presented in the literature. In this paper we present a new ILP formulation for routing and wavelength allocation, under the scheduled traffic model that minimizes the congestion of the network. We propose two levels of service, where idle backup resources can be used to carry low priority traffic, under fault-free conditions. When a fault occurs, and resources for a backup path need to be reclaimed, any low priority traffic on the affected channels is dropped. The results demonstrate that this can lead to significant improvements over single service level models. We are able to generate optimal solutions for moderate sized networks, within a reasonable amount of time. Finally, we present a simple and fast heuristic that can quickly generate good solutions for much larger networks.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 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