Optimal routing and wavelength assignment for survivable multifibre WDM networks
Bibliographic record
Abstract
Multifibre optical networks use a bundle of fibres to realize a link between two optical nodes. Such networks can offer significant economic benefits over single-fibre networks because of their ability to relax the restrictions imposed by the wavelength continuity constraint and their potential for handling future growth. This paper introduces two new and efficient integer linear program (ILP) formulations for dynamic wavelength allocation in survivable multifibre wavelength-division multiplexing (WDM) networks, using dedicated and shared protection. Single-fibre networks, both with and without wavelength conversion, can be treated as a special case of these formulations. The new formulations have been tested on several well-known WDM networks, and the results have been compared to those for single-fibre networks. A simple heuristic for dynamic lightpath allocation is also proposed, and its performance is validated by a comparison of the results to optimal solutions generated by the ILPs. Experimental results demonstrate that the new ILPs are feasible for current networks under low-to-medium traffic. For very large or highly congested networks, the heuristic can be used.
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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 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".