Comparative Analysis on Cluster-based Algorithms for Failure Management in Optical Networks

Rafael Sales, Andrei Nogueira Ribeiro, Fabrício Lobato, Moisés Felipe Silva, Joao Weyl Costa

DOI: 10.14209/sbrt.2024.1571035792

Evento: XLII Simpósio Brasileiro de Telecomunicações e Processamento de Sinais (SBrT2024)

Keywords: Clustering Failure Management Unsupervised Learning Optical Networks

Abstract

The occurrence of failures in transparent optical networks (TONs) potentially compromises their reliability, leading to loss of information or even link disruption. In that regard, as the conventional methods for failure management become difficult with the increasing complexity of TONs, the use of machine learning algorithms has emerged as an alternative strategy given their sole capabilities concerning network scalability and self-awareness. Therefore, in this paper, the failure management performance of three cluster-based algorithms (k-means, fuzzy c-means, and Gaussian mixture models) are compared on a real-world testbed in terms of failure classification errors.

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