On Board-Level Failure Localization in Optical Transport Networks Using Graph Neural Network
Bibliographic record
Abstract
This paper investigates a novel framework for board-level failure localization in the Optical Transport Networks (OTN), dubbed Board-Alarm Propagation Tree based Failure Localization (BAPT-FL). Foremost, a collection of functional graphs (FGs) is garnered by iteratively tagging each board in the network topology, serving as the ground of the proposed framework. Concretely, BAPT-FL is designed to build a range of BAPTs by correlating the tagged boards and alarms involved in the FGs, where each BAPT deems a failed board and its corre-lated alarms as the root and leaves, respectively. To evaluate the edge weights of potential BAPTs induced by FGs, a graph neural network (GNN) with the graph transformer operator is employed as an edge classifier, which characterizes each vertex/edge from diverse dimensions including time, traffic distribution, network topology, and board/alarm attributes. Subsequently, we frame an integer linear programming (ILP) problem to construct the best possible BAPT(s). Extensive case studies are conducted to showcase BAPT-FL's advantage over its counterparts in terms of the metrics assessing the identified failed boards/root alarms. We also delve into its performance in volatile environmental variations such as diverse failure scenarios, network topologies, traffic distributions, and noise alarms.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 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) | 0.001 | 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".