CAPEX/OPEX Effective Optical Wide Area Network Design
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| 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 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it