Monitoring trail allocation for fast link failure localization without electronic alarm dissemination
Bibliographic record
Abstract
Monitoring trail (m-trail) provides an efficient way to achieve fast and unambiguous link failure localization in all-optical networks. To remove the electronic alarm dissemination, the original m-trail concept has been extended to allow trail status checking at each on-trail node by tapping the supervisory optical signal. By properly allocating such extended m-trails, each monitoring node can ail-optically localize every link failure using its locally collected optical alarm signals. This not only speeds up failure localization, but also minimizes monitoring resources by sharing supervisory wavelength-links among different monitoring nodes. However, the existing ILP design is very time-consuming and could hardly reach optimality. In this paper, we propose an efficient greedy algorithm to allocate the (extended) m-trails and minimize the total wavelength cost Our heuristic is based on a novel “Min Wavelength Max Information” principle which quantifies the contribution of each m-trail on failure localization, and a set of advanced techniques (such as trail-splitting and trail-sharing, etc) to intelligently allocate m-trails. Simulation results substantially attest the superior efficiency and performance of the algorithm in terms of minimizing the total wavelength cost, the required number of m-trails, and the algorithm running time.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".