Machine Learning Model for Traffic Prediction and Pattern Extraction in High-Speed Optical Networks

The tremendous development of network traffic causes the need to develop new network applications. Machine learning provides a pertinent platform to enhance currently used network optimization methods. The information about future traffic volumes is vital for the network operators. This paper presents an issue in traffic prediction and pattern extraction in high-speed optical networks and fills the literature gap by using a multiprocessor module from Python to enhance the efficiency of the work. An ML approach based on regression is designed. In the investigation, a dataset has been simulated and generated using the SNDLIB library and Python module GNPy estimation tool, which provides dynamic traffic matrices stated for various real network topologies and mimics real-world data. The performance of the proposed system is evaluated using different evaluation indexes like Mean Square Error (MSE), Mean Absolute Error (MAE), Max error (ME), and processing time based on the tuning of hyper-parameters. Outcomes of findings indicate better results compared to the existing technique. The findings confirm that proposed approach’s efficiency is better and seems to be a promising solution to the current network problem.