Resilient traffic grooming for WDM networks
Why this work is in the frame
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Bibliographic record
Abstract
Traffic grooming techniques are used to combine low-speed individual requests for connections onto high-speed lightpaths in an efficient manner. Design of survivable grooming capable networks is of critical importance. For such networks, protection may take place at the lightpath level or at the connection level. However, optimal formulations for implementing protection at either level are computationally intractable and can only be used for very small networks. We present an efficient integer linear program (ILP) formulation for the complete survivable traffic grooming problem, including topology design, traffic routing, and routing and wavelength assignment, using both dedicated and shared protection at the lightpath level. Unlike existing formulations, our ILP is able to generate optimal solutions for practical sized networks with hundreds of traffic requests.
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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 | 0.001 |
| 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