CAPEX/OPEX Effective Optical Wide Area Network Design
Bibliographic record
Abstract
The focus of this paper is on the design of the so-called Optical Wide Area Networks (owans), i.e., optical networks that cover broad areas. Our objective is to investigate efficient owan network design, where demand provisioning takes full advantage of the nodal switching equipment and of the network interface platforms under asymmetric traffic. It involves granting all traffic requests while minimizing the network capital and operational expenses, throughout an optimal dimensioning of the nodal equipment, i.e., minimizing the number and the location of the network nodal equipment. The originality of our work is in the forethought and the investigation of these issues. We establish a mathematical model which makes use of large scale optimization tools and propose a column generation algorithm coupled with a rounding off heuristic in order to solve it efficiently. In our experiments, with different network and traffic instances, we show that a careful dimensioning and location of the nodal equipment can save up to 35% of capital expenses, and even more sometimes.
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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 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 0.001 |
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".