A Novel Framework of Fast and Unambiguous Link Failure Localization via Monitoring Trails
Bibliographic record
Abstract
The concept of monitoring trail (m-trail) has been proposed for achieving Fast and Unambiguous Link-failure Localization (FULL) in all-optical WDM (Wavelength Division Multiplexing) mesh networks. Previous studies on m-trails assumed the presence of alarm dissemination at each node such that a remote routing entity can collect the flooded alarm bits and form the alarm code to localize the failed link. This obviously leads to additional delay and extra control complexity in the electronic domain process. In this paper, we propose a novel framework based on m-trails for FULL, aiming at avoiding any possible alarm flooding and electronic domain mechanism such that each individual monitoring node (MN) can localize a single link failure according to locally available alarm bits. To save the supervisory wavelength-links, the proposed framework enables that the status of an m-trail can be monitored by multiple MNs along the m-trail by tapping the optical supervisory signal, rather than only by the destination node of the m-trail. An ILP (Integer Linear Program) is formulated and solved in a case study to verify the ILP and show the effectiveness of the proposed framework. We demonstrate that the status sharing among MNs of a common m-trail can effectively suppress the increase of supervisory wavelength-links as the number of MNs increases.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".