On Real-time Failure Localization via Instance Correlation in Optical Transport Networks
Bibliographic record
Abstract
Failure localization serves as a key to an effective fault management plane in the Internet backbone. This paper investigates a novel failure localization approach, namely Instance Correlation based Fault Diagnosis (IC-FD), for achieving efficient fault management in Optical Transport Networks (OTN). The IC-FD is aimed at real-time localization of failed components in the optical layer of OTN through correlation of alarms and status changes of network devices (referred to as instances) via a learned binary classifier. The outcome of IC-FD is one or multiple instance correlation trees (ICT) where the instances corresponding to the faulty network devices are taken as the tree roots. Notably, the proposed binary classifier is characterized by an intelligent feature extraction of historical instance correlation in dimensions of time, board/alarm attribute, network topology, and traffic distribution. Extensive case studies are conducted to demonstrate the advantages gained by IC-FD in terms of its high precision and low computation complexity, as well as analysis of its performance due to various environmental turbulence such as network topology, traffic diversity 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 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| 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) | 0.000 | 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 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".