A Comparative Study on Semi-Supervised Learning Algorithms for Fault Management in Optical Networks

In recent years, the demand for reliable optical networks has intensified due to the rise of bandwidth-intensive applications. Faults in these networks can degrade transmission quality, leading to packet loss and service disruptions, making effective failure management essential for maintaining network availability and compliance with Service Level Agreements (SLAs). While most contemporary approaches rely on supervised learning (SL), which requires large amounts of often scarce fault data for training, this study explores semi-supervised learning (Semi-SL) algorithms for fault management in optical networks, which rely only on normal operating condition data, thus reducing the dependency on scarce fault data. We evaluate several cluster-based algorithms and dimensionality reduction techniques using a dataset from an optical testbed. Results based on Type I/II errors show that all techniques performed with an average accuracy exceeding 90%. The Autoencoder yielded the best fault management performance (96.46% of average accuracy), followed by Gaussian Mixture Model (92.5%), Mahalanobis Squared-Distance (92.48%), Density-based spatial clustering of applications with noise (92.42%), Fuzzy C-means (92.12%), K-means (92.09%), and Principal Component Analysis (90.22%).