About the speakers
Sebastian Troia is Assistant Professor at the Department of Electronics, Information and Bioengineering at Politecnico di Milano (Italy) and a Fulbright Fellow at the Erik Jonsson School of Engineering and Computer Science at the University of Texas at Dallas (USA). In 2020, He earned his Ph.D. degree in Information Technology (Telecommunication area) cum laude from Politecnico di Milano. His current research interests are in the field of edge network softwarization and Machine Learning for communication networks. His work encompasses the development of intelligent control and orchestration plane architectures for SDN and SD-WAN in multi-layer (optical and IP) network scenarios. He has participated in various European Projects such as H2020 Metro-Haul, NGI Atlantic, and FP7 Marie Curie MobileCloud. Additionally, he served as an editor for the ITU Focus Group on Machine Learning for Future Networks including 5G (FG-ML5G). He has co-authored more than 50 publications in international journals, conferences and book chapters, with a focus on Machine-Learning for SDN, SD-WAN and NFV. He is a reviewer for several international journals, and a member of the Technical Program Committee for international conferences and workshops including IEEE ICC and IEEE GLOBECOM. He is Guest-Editor for Elsevier Computer Networks journal's Special Issue on: "Network Softwarization and Intelligence at the Edge: Challenges and Opportunities". He has been also a member of the Organizing Committee for IEEE HPSR 2019, DRCN 2020 and 2021, and IEEE NetSoft 2022. He is the co-organizer and general co-chair of the second edition of the Edge Network Softwarization (ENS) workshop, held in conjunction with IEEE NetSoft 2023 in Madrid, Spain.
Guido Maier received his Laurea degree in Electronic Engineering at Politecnico di Milano (Italy) in 1995 and his Ph.D. degree in Telecommunication Engineering at the same university in 2000. Until February 2006 he has been researcher at CoreCom (research consortium supported by Pirelli in Milan, Italy), where he achieved the position of Head of the Optical Networking Laboratory. On March 2006 he joined the Politecnico di Milano as Assistant Professor. In 2015 he became Associate Professor. His main areas of interest are: optical network modelling, design and optimization; SDN orchestration and control-plane architectures; SD-WAN and NFV. He is author of more than 150 papers in the area of Networking published in international journals and conference proceedings (h-index 26) and 6 patents. He is currently involved in industrial and European research projects. In 2016 he co-founded the start-up SWAN networks, spin-off of Politecnico di Milano. He is editor of the journal Optical Switching and Routing, guest editor of the IEEE Open Journal of the Communications Society, General Chair of DRCN 2020, DRCN 2021 and NetSoft 2022, and TPC member in many international conferences. He is a Senior Member of the IEEE Communications Society.
Abstract
This tutorial addresses the Software-Defined Wide Area Network (SD-WAN) technology, which has recently conquered the enterprise-networking market all over the world. SD-WAN is regarded as very promising for the next-generation WANs, especially by the Communication Service Providers (CSPs) as a new highly effective solution they can offer to their customers. SD-WAN brings the advantages of SDN to the WAN, applying the concept of separation among data and control plane. The main goal is to provide dynamic, fast and reliable interconnections between the sites of an organization, such as headquarters, data-centers, branch offices, that are geographically distributed over a wide area. A communication infrastructure with a national or international or even global extension can thus be provided to the tenants as an overlay network over heterogeneous public WANs. SD-WAN reduces the costs, but has to preserve the same quality of service of alternative, more expensive technologies, such as MPLS. We will present a detailed overview of SD-WAN by addressing the most important use cases, such as enterprise branch-to-headquarter and headquarter-to-data-center switching interconnection. In particular, we will focus on the network architecture requirements in order to obtain an agile and efficient control plane. Afterwards, we will describe the decision techniques that can be implemented inside the SD-WAN controller, making a comparison between traditional and Machine-Learning solutions.