Performance optimization for fault localization in all-optical networks
Bibliographic record
Abstract
Fault localization is an important issue in all-optical networks. The Limited Perimeter Vector Matching (LVM) protocol is a novel fault localization protocol for localizing single-link failures in all-optical networks. In this paper, we study the fault localization optimization problem in applying the LVM protocol to static networks, where traffic (or lightpath) demand is known a priori. Given the traffic demand, the fault localization optimization problem is to optimize the traffic distribution so that the fault localization probability in terms of the number of localized links can be maximized. We formulate the problem into an integer linear programming problem and use CPLEX to solve the problem. We show through numerical results that by optimizing the traffic distribution the fault localization probability in terms of the number of localized links can be maximized. Moreover, the solution to the problem can also provide the maximum number of wavelengths needed on each link to obtain the maximum fault localization probability.
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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".