Operation research tools and methodology for the design and provisioning of survivable optical networks
Bibliographic record
Abstract
While the design and the provisioning of survivable optical networks have already been studied for a long time, networks across the world are still experiencing a phenomenal growth in data traffic, leading to more complex design and provisioning problems. Network architectures are changing rapidly to meet the new end-user requirements, with many new technovirtual developments and new economical/environmental concerns such as, e.g., network virtualization, anycast routing, elastic networking, energy minimization. While operational research methods have made significant progress over the last 20 years, not much has been done in order to use the full potential of these developments in managing more efficiently communication networks, while, in other areas of applications, they have been used to solve efficiently very large/huge scale optimization problems, e.g., in the transport industry or in financial engineering or in industrial location. This paper gives an overview of some of the developments in solving some optimization problems arising in the design of optical networks or grids. In terms of optimization techniques, we will focus on decomposition techniques, and will discuss their recent success for the protection of optical networks, and of virtual networks built upon optical networks/grids.
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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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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".