Deep learning based network traffic matrix prediction
Dalal Al-Oraifan, Imtiaz Ahmad, Ebrahim A. Alrashed
Abstract
Network traffic matrix prediction is a methodology of predicting network traffic behavior ahead of time in order to improve network management and planning. Different neural network models ranging from simple recurrent neural network (RNN) to long short-term memory neural network (LSTM) and gated recurrent unit (GRU) are being used to predict traffic matrix. In this paper, for the first time the bidirectional LSTM (Bi-LSTM) and the bidirectional GRU (Bi-GRU) are applied to predict the network traffic matrix due to their high effectiveness and efficiency. The proposed models were designed as hybrid models that support multiple neural network models in a chained manner to support higher feature learning and subsequently higher accuracies in traffic matrix prediction. The hybrid models combined convolutional neural network (CNN) with either Bi-LSTM or Bi-GRU along with the unidirectional versions. With this approach, it gives the ability to eliminate unneeded information in order to obtain good data prediction. The comparisons of the proposed methods were applied on real traffic data from the GÉANT network. The results showed that the proposed models have a considerable improvement in prediction accuracy when compared to other existing models found in literature.