TY - GEN
T1 - Fault Prediction for Optical Access Network Equipment using Decision Tree Methods
AU - Murphy, K.
AU - Lavignotte, A.
AU - Lepers, C.
N1 - Publisher Copyright:
© 2023 IEEE.
PY - 2023/1/1
Y1 - 2023/1/1
N2 - 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.
AB - 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.
KW - Optical communication networks
KW - machine learning
KW - network failure prediction
KW - network fault management
KW - network fault prediction
KW - network maintenance
KW - network reliability
KW - photonics
UR - https://www.scopus.com/pages/publications/85183296772
U2 - 10.1109/ACP/POEM59049.2023.10369987
DO - 10.1109/ACP/POEM59049.2023.10369987
M3 - Conference contribution
AN - SCOPUS:85183296772
T3 - 2023 Asia Communications and Photonics Conference/2023 International Photonics and Optoelectronics Meetings, ACP/POEM 2023
BT - 2023 Asia Communications and Photonics Conference/2023 International Photonics and Optoelectronics Meetings, ACP/POEM 2023
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2023 Asia Communications and Photonics Conference/2023 International Photonics and Optoelectronics Meetings, ACP/POEM 2023
Y2 - 4 November 2023 through 7 November 2023
ER -