Origin Destination Traffic Matrix Prediction in Networks using Recurrent Layer Algorithms

Abstract

Origin Destination (OD) Traffic Matrix (TM) contains the number of packets transferring among the nodes at a particular time in a confined network. This TM can be used to schedule traffic, manage peak traffic, make network rules, identify suspicious activities, traffic routing, etc., for which the prediction should be more accurate. Traditionally Machine Learning (ML) algorithms have played a crucial role in predicting the time series data such as TMs. In this paper, four different ML algorithms, namely Long Short-Term Memory (LSTM), Gate Recurrent Unit (GRU), Simple Recurrent Neural Network (SRNN), and Bidirectional LSTM (BiLSTM), are applied, which use recurrent layers in neural networks, to the three real-world TM data sets such as American Research and Education Network (Abilene), Europe Research and Education Network (GÉANT), China Education and Research Network (CERNET) and a custom testbed generated TM data set. The final results show that the tested algorithms make predictions with minimum errors.