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Communication Dans Un Congrès Année : 2024

Fault prediction for optical access network equipment using decision tree methods

Résumé

Predicting optical equipment failures reliably before they happen, offers the promise to significantly improve network Quality of Service (QoS) and to reduce maintenance costs. State-of-the-art prediction methods for Network Fault Prediction are not easily comparable. In this paper, a Decision-Tree based Machine Learning benchmark for optical Network Fault Prediction alarms is presented based on a real-world dataset. The Machine Learning architectures are compared with a fixed lead time between the prediction and the window for which alarms are considered. Precision, recall, F1-score metrics, a cost function representing theoretical monetary gain are used for the comparison. Additionally, a Quality of Service gain metric is proposed and used for the comparison. Perspectives for future research are proposed.
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hal-04391064 , version 1 (12-01-2024)

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Killian Murphy, Antoine Lavignotte, Catherine Lepers. Fault prediction for optical access network equipment using decision tree methods. 2023 Asia Communications and Photonics Conference/2023 International Photonics and Optoelectronics Meetings (ACP/POEM), Nov 2023, Wuhan, China. pp.1-5, ⟨10.1109/ACP/POEM59049.2023.10369987⟩. ⟨hal-04391064⟩
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