Short-Term Network Traffic Prediction with Multilayer Perceptron

Abstract

The constantly increasing internet traffic and rising network requirements trigger fast development and implementation of new networking architectures and technologies. Predictability of network traffic can bring significant benefits in many areas, such as network planning, network security, dynamic bandwidth allocation, and predictive congestion control. This paper studies the problem of short-term traffic forecasting in application-aware backbone optical networks. The proposed method is based on the Multilayer Perceptron (mlp). Multiple neural network architectures are evaluated using three datasets with diverse characteristics, representing different types of internet traffic in a real-world backbone network. An extensive examination is performed to find the best neural network architecture for each traffic type. The proposed method revealed high prediction quality, achieving the mean absolute percentage errors between 2% and 10%, depending on the traffic type. The proposed neural networks outperform the baseline regression model in all considered types of traffic.