Traffic prediction based on machine learning for elastic optical networks

The increased data transfers and rapidly evolving cloud services lead to the inevitable need for the new techniques applied to communication networks, such as AI, machine learning, and data analysis. In this paper, we present two approaches that employ the machine learning techniques to enable traffic prediction in Elastic Optical Networks. Results show that the application of adaptive strategies has superior performance, which is a future opportunity for telecommunication operators to improve the efficiency of their network architectures.