A Resilient Transparent Optical Network Design with a Pre-Configured Extended-Tree Scheme
Bibliographic record
Abstract
We propose a new design scheme of resilient wavelength division multiplexing (WDM) networks by extending and reshaping pre-configured protection tree (p-tree) structures. The resulting protection scheme relies on optimized pre-cross connected structures that span all previously proposed protection patterns. p-tree-based protection schemes offer the advantages of scalability, local restoration capabilities, and failure impact restriction, but at the same time suffer from capacity inefficiency. While keeping these advantages, we propose an extension (reshaping) of the p-tree protection pattern that imposes no restriction on the shapes of the protection building blocks. Not only the resulting protection scheme remains scalable and highly flexible, but it also leads to pre-configured protection structures that improve much further on capacity efficiency and recovery delay. We establish some new integer linear programming models, and use a large scale optimization tool, named column generation (CG) to solve them. Our CG-based solution method is highly scalable as it does not require an a priori explicit enumeration of the protection structures, but an efficient dynamic enumeration of only the most promising ones. Comparison are made with three other protection schemes, i.e, simple and non-simple p-cycles (fully pre-cross connected structures) as well as p-trees. Results show a clear advantage of the proposed extended-tree scheme with respect to flexibility, capacity efficiency, and restoration delay.
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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 | 0.000 | 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 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".