How Much Wavelength Conversion Allows a Reduction in the Blocking Rate
Bibliographic record
Abstract
We study the problem of routing and wavelength assignment (RWA) in a WDM optical network under different hop assumptions, i.e., with and without wavelength converters, considering the objective of minimizing the blocking rate. We design a heuristic with two interactive phases, one for the routing and one for the wavelength assignment, which generalizes a previous algorithm by Noronha and Ribeiro [Eur. J. Oper. Res. 171, 797 (2006)] based on a Tabu Search scheme using a partition coloring reformulation for uniform traffic and single-hop connections. Considering nonuniform traffic, we explore a reformulation of the RWA problem as a generalized partition coloring problem and develop a Tabu Search algorithm to solve it. We also explore how to integrate multihop connections, with the addition of conversion features at some or at all optical nodes. Experiments are done on several traffic and network instances. Most heuristic solutions are excellent as illustrated by the very small gap between the values provided by the heuristic and the optimal values of the linear relaxation. We next show that conversion features, although often considered an added value, are of little help in improving on the blocking rate except for some very particular traffic instances, even on realistic network topologies.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".