SRLG fault localization in all-optical networks
Bibliographic record
Abstract
This paper introduces a novel m-trail allocation method for achieving local unambiguous failure localization (L-UFL) in all-optical mesh networks under the monitoring burst (m-burst) framework, in which a single monitoring node (MN) can localize any multi-link failure with up to d links in a (d+1)-connected network by inspecting the optical bursts traversing through the MN where each m-trails is originated from the MN. The proposed m-trail allocation method is based on the theory that when each undirected link is traversed by a unique set of m-trails and the m-trail set is not a subset of the m-trails traversing any shared risk link group (SRLG) having d links whenever the SRLG is disjoint from the link, each SRLG having 1 to d links will be traversed by a unique set of m-trails. We prove the theorem for m-trail allocation, formulate an integer linear program (ILP) based on the theorem, and implement the method for up to 3-link failures. Numerical results show that the proposed method outperform the previous arts that do not use post-processing to reduce the m-trails in the solution.
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How this classification was reachedexpand
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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".