Traffic estimation via Kalman filtering under partial information in software-defined networks

Abstract

An accurate traffic matrix (TM) is essential for network design, management and optimization. Software-defined networking (SDN) provides flow level statistics with global centralized control which enables construction of more accurate traffic matrices. However, retrieving all the flow statistics can cause a very significant overhead in the system. In this work, we propose an inference framework which utilizes Kalman filtering to create an accurate and timely traffic matrix. In our scheme, only a small number of flow statistics are measured at a time, yet the estimate of the TM is highly accurate. Besides, we propose a switch selection strategy which aims to minimize the entropy of the estimate, that is to maximize the information obtained by the estimation. Using simulation-based experiments, we show that our framework provides a very accurate TM estimate compared to the one constructed by using all the flow statistics in the network.