Experimental Investigation of Machine Learning based Soft-Failure Management using the Optical Spectrum
This work addresses adaptive fault management for complex optical networks, representing an incremental improvement with specific gains in data efficiency and unknown failure identification.
The paper tackled soft-failure management in optical networks by experimentally comparing machine learning algorithms and introducing a VAE-GAN framework, which outperformed others with up to 10% of training data and achieved high F1-scores for unknown failures.
The demand for high-speed data is exponentially growing. To conquer this, optical networks underwent significant changes getting more complex and versatile. The increasing complexity necessitates the fault management to be more adaptive to enhance network assurance. In this paper, we experimentally compare the performance of soft-failure management of different machine learning algorithms. We further introduce a machine-learning based soft-failure management framework. It utilizes a variational autoencoder based generative adversarial network (VAE-GAN) running on optical spectral data obtained by optical spectrum analyzers. The framework is able to reliably run on a fraction of available training data as well as identifying unknown failure types. The investigations show, that the VAE-GAN outperforms the other machine learning algorithms when up to 10\% of the total training data is available in identification tasks. Furthermore, the advanced training mechanism for the GAN shows a high F1-score for unknown spectrum identification. The failure localization comparison shows the advantage of a low complexity neural network in combination with a VAE over established machine learning algorithms.