Predicting Traffic Demand Matrix by Considering Inter-flow Correlations

Abstract

Accurate traffic demand matrix (TM) prediction is essential for effective traffic engineering and network management. Based on our analysis of real traffic traces from Wide Area Network (WAN), the traffic flows in TM are both time-varying (i.e. with intra-flow dependencies) and correlated with each other (i.e. with inter-flow correlations). However, existing works in TM prediction ignore inter-flow correlations. In this work, we propose a new model for TM prediction by considering both inter-flow correlation and intra-flow dependencies, namely CRNN model. By observing that most strongly-correlated traffic flows have the same source or destination, we employ convolutional structures to capture the neighboring correlation in TMs and extract even higher-order correlations. To model the trends in time dimension, like previous works we use recurrent structures to capture the temporal variations of traffic flows. Our evaluation on two WAN datasets shows that, when predicting the next TM, CRNN model reduces the Mean Squared Error (MSE) by up to 44.8% compared to state-of-the-art method; and the gap is even larger when predicting the next multiple TMs.