Efficient Modeling of the Routing and Spectrum Allocation Problem for Flexgrid Optical Networks
Bibliographic record
Abstract
While the problem of Routing and Spectrum Allocation (RSA) has been widely studied, very few studies attempt to solve realistic sized instances. Indeed, the state of the art is always below the standard transport capacity of a fiber link with 384 frequency slots, regardless of what the authors consider, heuristics or exact methods with a few exceptions. In this paper, we are interested in reducing the gap between realistic data sets and testbed instances that are often considered, using exact methods. Even if exact methods may fail to solve in reasonable time very large instances, they can, however, output solutions with a very good and proven accuracy. The novelty of this paper is to exploit the observations that optimal solutions contain a very large number of lightpaths associated with shortest paths or k-shortest paths with a small k. We propose an original efficient large-scale optimization model and decomposition algorithm to solve the RSA problem for flexgrid optical networks. It allows the exact or near optimal solution of much larger instances than in the literature.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".