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 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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".