Failure Localization for Shared Risk Link Groups in All-Optical Mesh Networks Using Monitoring Trails
Bibliographic record
Abstract
This paper considers the problem of out-of-band failure localization in all-optical mesh networks using bidirectional monitoring trails (bm-trails), where every possible link set with up to <i xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">d</i> arbitrary links is considered as a shared risk link group (SRLG). With the SRLG scenario, the bm-trail allocation problem is firstly formulated, which includes the phases of code assignment and bm-trail formation. In the first phase, each SRLG is uniquely coded by assigning each link with a nonadaptive d̅-separable combinatorial group testing code. Then, the second phase manipulates a sophisticated yet efficient bm-trail formation process through a novel greedy code-swapping mechanism, such that any SRLG failure can be unambiguously localized by collecting the alarms of the interrupted bm-trails. The algorithm prototype can be found in . Extensive simulation is conducted on hundreds of randomly generated planar topologies to verify the proposed approach in terms of the number of required bm-trails and the computational efficiency. Our approach is compared with previously reported counterparts, by which its merits are further demonstrated.
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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 | 0.001 | 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".