Optimal regenerator placement in survivable translucent networks
Bibliographic record
Abstract
In optical networks, the optical reach is defined as the distance an optical signal can travel, before its quality degrades to a level that requires 3R-regeneration. In a translucent optical network, if an optical signal has to be communicated over a distance that exceeds the optical reach, the signal is regenerated at selected nodes of the network, so that the signal quality never degrades to an unacceptable level. Given a value of the optical reach, the goal of the Regenerator Placement Problem (RPP) in dynamic Physical Impairment aware Route and Wavelength Assignment (PI-RWA), for survivable translucent networks, is to identify the minimum number of nodes capable of 3R regeneration, so that every pair of nodes (u, v) can establish a lightpath (either transparent or translucent) from u to v. In a survivable network, even if any fault occurs, it must be guaranteed that every pair of surviving nodes (u, v) can still establish a lightpath (either transparent or translucent) from u to v, avoiding all faulty nodes/edges. In this paper we have presented a Integer Linear Program (ILP) formulation that can optimally solve the survivable RPP problem for practical-sized networks within a reasonable amount of time. We have used a branch-and-cut approach to implement our algorithm, where we have intercepted the optimization process with control callbacks from the CPLEX callable library to introduce new constraints, as needed.
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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 | 0.000 | 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) | 0.003 | 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".