Bibliographic record
Abstract
While lightpath rearrangement has already been investigated by several authors for the dynamic RWA problem, we propose to revisit it with the goal of evaluating the minimum number of lightpath rearrangement it requires in order to remain with an optimized RWA provisioning, using ε-optimal solutions. Lightpath rearrangement is now made feasible with the use of colorless, directionless, and contentionless (CDC) reconfigurable optical add/drop multiplexers (ROADMs) in optical networks. While exact solution of the RWA problem was out of reach few years ago, it is now possible for fairly large data instances, i.e., with up to 150 wavelengths, in few minutes of computing times. We investigate how much bandwidth is wasted when no lightpath rearrangement is allowed, and compare it with the number of lightpath rerouting it requires in order to fully maximize the grade of service (GoS). Experiments are conducted on several data instances with up to 150 wavelengths. Results show that the amount of lightpath rearrangement varies with the size of the network, but in any case, remains very small in comparison to the amount of wasted bandwidth if not done.
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".