Monitoring trail allocation for fast link failure localization without electronic alarm dissemination
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
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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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 | 0.000 |
| 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 | 0.000 |
| 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