A proposal for a semi-dynamically reconfigurable optical network optimization
Bibliographic record
Abstract
The Routing and Wavelength Assignment (RWA) problem has attracted lots of attention in the research field for the past decade. Most of the existing works are the classic static RWA problem, which assumes every time for the reconfiguration, all the existing connections will be reconfigured. In a real operating network, the reconfiguration has to take the existing connections into consideration and any reconfiguration of the existing connection results in the disruption of the upper level traffic. The algorithms that are slow or do not consider the existing connections in the network cannot be used in the real-time reconfigurable network. In this paper, we propose the semi-dynamic/static network optimization problem that takes into consideration existing connections from the previous reconfiguration session. The objective function in the formulation is penalty-based, i.e., there are penalties for the reconfiguration of a connection, for the rejection of a connection demand and for the most congested link. Rules on the existing capacity and new demand in the new session are proposed. We have successfully used the Lagrange Relaxation (LR) and Subgradient Method to successfully solve this network optimization problem. This state-of-art frame work allows us to evaluate systematically some sample networks in terms of various network performances and behaviors. At the same time, excellent algorithm performance and efficient computation complexity are demonstrated.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".