A Novel Framework for Optical Layer Device Board Failure Localization in Optical Transport Network
Bibliographic record
Abstract
This paper presents a novel framework called Failure-Alarm Correlation Tree based Failure Localization (FACT-FL), designed to localize failed optical layer device boards in an Optical Transport Network (OTN). Specifically, FACT-FL aims to construct a set of FACTs by correlating the failed boards and alarms, where each FACT takes one failed board and its correlated alarms as the root and leaves, respectively. Furthermore, a FACT consists of a suite of kth order Failure-Alarm Correlation Chains (k-FACCs) with different order values of k. Each k-FACC indicates the chain-like correlation established by k alarms due to one common failed board. To identify all previously undetected k-FACCs, a set of binary classifiers is trained that characterizes each k-FACC from various dimensions, including time, network topology, traffic distribution, and board/alarm attributes. Eventually, an integer linear programming (ILP) problem is formulated to extract the most likely FACT(s) from those k-FACCs. Extensive case studies demonstrate the superior results of FACT-FL in terms of metrics evaluating the identified failed boards and root alarms. We also analyze its performance under different maximum order values of k and environmental changes, including 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 | 0.000 |
| 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".