Meta-learning-aided QoT estimator provisioning for a dynamic VNT configuration in optical networks

Machine learning (ML)-based quality-of-transmission (QoT) estimation tools will be desirable for operating virtual network topologies (VNTs) that disclose only abstracted views of connectivity and resource availability to tenants. Conventional ML-based solutions rely on laborious human effort on mod...

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Vydáno v:Journal of optical communications and networking Ročník 17; číslo 1; s. A103 - A111
Hlavní autoři: Chen, Xiaoliang, Ouyang, Zhenlin, Gao, Hanyu, Lin, Qunzhi, Zhu, Zuqing
Médium: Journal Article
Jazyk:angličtina
Vydáno: Piscataway Optica Publishing Group 01.01.2025
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Témata:
Accuracy
Automation
Configuration management
Data models
Estimation
Graph neural networks
Heuristic algorithms
Machine learning
Maximum likelihood estimation
Network topologies
Optical fiber networks
Provisioning
Substrates
Training
Tuning
Virtual networks
ISSN:1943-0620, 1943-0639
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Shrnutí:Machine learning (ML)-based quality-of-transmission (QoT) estimation tools will be desirable for operating virtual network topologies (VNTs) that disclose only abstracted views of connectivity and resource availability to tenants. Conventional ML-based solutions rely on laborious human effort on model selection, parameter tuning, and so forth, which can cause prolonged model building time. This paper exploits the learning-to-learn nature by meta learning to pursue automated provisioning of QoT estimators for a dynamic VNT configuration in optical networks. In particular, we first propose a graph neural network (GNN) design for network-wide QoT estimation. The proposed design learns global VNT representations by disseminating and merging features of virtual nodes (conveying transmitter-side configurations) and links (characterizing physical line systems) according to the routing schemes used. Consequently, the GNN is able to predict the QoT for all the end-to-end connections in a VNT concurrently. A distributed collaborative learning method is also applied for preserving data confidentiality. We train a meta GNN with meta learning to acquire knowledge generalizable across tasks and realize automated QoT estimator provisioning by fine tuning the meta model with a few new samples for each incoming VNT request. Simulation results using data from two realistic topologies show our proposal can generalize QoT estimation for VNTs of arbitrary structures and improves the estimation accuracy by up to 18.7% when compared with the baseline.
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ISSN:1943-0620
1943-0639
DOI:10.1364/JOCN.534417