Distributed fault localization for multi-domain all-optical networks without power monitoring
Bibliographic record
Abstract
In this paper, we propose a distributed fault localization protocol for localizing single-link failures in all-optical multi-domain networks. The protocol is based on a limited-perimeter vector matching (LVM) mechanism, which restricts fault localization within a smaller perimeter area and can thus significantly reduce fault localization time. By performing a fault localization process sequentially in the optical domains that an affected lightpath passes through, the protocol can localize both inter-domain and intra-domain link failures that affect inter-domain traffic without exchanging any internal confidential domain-specific information between different domains. We show through analytical results that it can not only fast localize an inter-domain link failure between different domains but also localize an intra-domain link failure that affects inter-domain traffic faster than the Open-Shortest-Path-First (OSPF) protocol in a large network.
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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".