Optimization for Fault Localization in All-Optical Networks
Bibliographic record
Abstract
Fault localization is a critical issue in all-optical networks. The limited-perimeter vector matching (LVM) protocol is a novel fault-localization protocol proposed for localizing single-link failures in all-optical networks. In this paper, we study the optimization problems in applying the LVM protocol in static all- optical networks. We consider two optimization problems: one is to optimize the traffic distribution so that the fault-localization probability in terms of the number of localized links is maximized, and the other is to optimize the traffic distribution so that the time for localizing a failed link is minimized. We formulate the two problems into an integer linear programming problem, respectively, and use the CPLEX optimization tool to solve the formulated problems. We show that by optimizing the traffic distribution the fault-localization probability can be maximized and the fault-localization time can be minimized. Moreover, a heuristic algorithm is proposed to evaluate the optimization results through simulation experiments.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| 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".