Designing reliable WDM networks — A global approach
Bibliographic record
Abstract
In this paper, we address the global problem of designing reliable wavelength division multiplexing (WDM) networks including the traffic grooming. This global problem consists in finding the number of optical fibers between each pair of optical nodes, finding the configuration of each node with respect to transponders, finding the virtual topology (i.e., the set of lightpaths), routing the lightpaths, grooming the traffic (i.e, grouping the connections and routing them over the lightpaths) and, finally, assigning wavelengths to the lightpaths. Instead of partitioning the problem into subproblems and solving them successively, we propose a mathematical programming model that addresses it as a whole. This approach has the advantage of providing better results since, in general, optimal solutions to all subproblems do not provide an optimal solution to the global problem. Numerical results show the relevance of designing the physical layer and finding the traffic grooming simultaneously.
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How this classification was reachedexpand
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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".