Learning Traffic Encoding Matrices for Delay-Aware Traffic Engineering in SD-WANs

This paper introduces Traffic Encoding Matrices (TEMs) as an alternative to traditional Traffic Matrices (TMs) for representing network traffic demands. While TMs only capture average demand information, TEMs are designed to capture distributional demand information by learning representations whose variations capture most of the structure of the distribution of demands. We present a practical approach based on off-the-shelf neural autoencoders to efficiently construct TEMs at the edge of the network. We then present the design and evaluation of NeuroTE, a DRL-based framework for delay-aware traffic engineering in SD-WANs using TEMs. Using real traffic traces, we present experimental results to demonstrate the advantages of using TEMs instead of TMs in traffic engineering. Our results show that, when traffic demands are dynamic, TEM-based control leads to: 1) improved network performance, and 2) faster convergence to the optimal solution compared to TM-based control using exactly the same DRL control algorithm.