On Real-time Failure Localization via Instance Correlation in Optical Transport Networks
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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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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 it