Traffic Matrix Prediction and Estimation Based on Deep Learning for Data Center Networks
Network traffic analysis is a crucial technique for systematically operating a data center network. Many network management functions rely on exact network traffic information. Although a great number of works to obtain network traffic have been carried out in traditional ISP networks, they cannot be employed effectively in data center networks. Motivated by that, we focus on the problem of network traffic prediction and estimation in data center networks. We involve deep learning techniques in the network traffic prediction and estimation fields, and propose two deep architectures for network traffic prediction and estimation, respectively. We first use a deep architecture to explore the time varying property of network traffic in a data center network, and then propose a novel network traffic prediction approach based on a deep belief network and a logistic regression model. Meanwhile, to deal with the highly ill pose property of network traffic estimation, we further propose a network traffic estimation method using the deep belief network trained by link counts. We validate the effectiveness of our methodologies by real traffic data.
2016 IEEE Globecom Workshops (GC Wkshps)