SRLG failure localization using nested m‐trails and their application to adaptive probing
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Bibliographic record
Abstract
This article explores a recently introduced novel technique called the nested monitoring trail (m‐trail) method in all‐optical mesh networks for failure localization of any shared risk link group (SRLG) with up to undirected links. The nested m‐trail method decomposes each network topology that is at least ‐connected into virtual cycles and trails, in which sets of m‐trails that traverse through a common monitoring node (MN) can be obtained. The nested m‐trails are used in the monitoring burst (m‐burst) framework, in which the MN can localize any SRLG failure by inspecting the optical bursts traversing through it. An integer linear program (ILP) and a heuristic are proposed for the network decomposition, which are further verified by numerical experiments. We show that the proposed method significantly reduces the required fault localization latency compared with the existing methods. Finally, we demonstrate that nested m‐trails can also be used in adaptive probing to find SRLG faults in all‐optical networks. The nested m‐trail based probing method needs a significantly reduced number of sequential probes. Thus, the method overcomes one of the important hurdles to deploy adaptive probing in all‐optical networks: the large number of sequential probes needed to localize SRLG faults. © 2015 Wiley Periodicals, Inc. NETWORKS, Vol. 66(4), 347–363 2015
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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