On Short- and Long-Term Traffic Prediction in Optical Networks Using Machine Learning

Abstract

In this paper, we formulate the problem of traffic prediction in optical networks. We then design a machine learning approach based on Graph Convolutional Network and the Generative Adversarial Network to enable efficient network states forecasting. The main focus is on detecting the peak traffic in networks that can affect the routing decisions. We validate our results using pseudorealistic datasets generated in a custom simulator and real networks provided by the network operator. The findings confirm our approach's efficiency for optimizing both the real-time routing and long-term network design decisions.

Topics & Concepts