An AI-based Traffic Matrix Prediction Solution for Software-Defined Network

Traffic Matrix (TM) clearly describes the volume and the distribution of traffic flows inside a network. TM plays an important role in many network management fields, such as traffic accounting, short-time traffic scheduling or re-routing, network design, anomaly detection, etc. Hence, an accurate TM prediction strategy is essential to handle those tasks effectively. Fortunately, Artificial Intelligence (AI) has been developing very strongly, thanks to computer technology developments such as GPU and TPU. That offers an opportunity to apply AI to TM prediction methods. However, applying Machine Learning techniques in traditional networks encounters some issues due to the distributed control and restricted local view of network nodes. For this purpose, a centralized control architecture, e.g., Software-defined Network (SDN), is a promising candidate. In this paper, we apply Long Short-Term Memory (LSTM) and its two variants, Bidirectional LSTM (BiLSTM) and Gate Recurrent Unit (GRU), for TM prediction mechanisms of an SDN architecture network. The prediction models have been evaluated using two datasets: the popular GÉANT backbone network traffic data and our dataset generated through a testbed. The experimental results show that our approach yielded promising traffic prediction accuracy.