SRLG failure localization using nested m‐trails and their application to adaptive probing
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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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 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".