23rd Conference on Optical Network Design and Modelling
May 13-16, 2019
Ramon Casellas, CTTC, Spain
Ramon Casellas graduated in Telecommunications Engineering in 1999 both from Technical University of Catalonia (UPC, Barcelona) and from the Ecole Nationale Supérieure des Télécommunications (ENST, Paris, now Telecom Paristech). He completed a PhD degree in Telecommunications in 2002, from the Ecole Nationale Supérieure des Télécommunications (ENST, Paris) funded by a CTI project with France Telecom Research and Development. He has worked as an undergraduate researcher at France Telecom Research and Development – FT R&D formerly known as Centre Nationale d’Etudes des Télecommunications (Issy les Molineaux, France) and British Telecom Labs (Ipswich, UK). In 2002, he joined the Networks and Computer Science Department at the ENST as an Associate Professor (Maïtre de Conférences) and, in March 2006, he joined the CTTC Optical Networking Area, where he currently holds a Senior Researcher position. His research interest areas include GMPLS/PCE architecture, Software Defined Networking (SDN), Network Function Virtualization (NFV), Traffic Engineering and Distributed control schemes, with applications to Optical and Disaggregated Transport Networks. He has participated in over 40 R&D projects funded by the European Commission’s Framework Programmes (H2020, FP7, FP6, CELTIC), including leading work-packages related to control plane and SDN (FP7 ICT IP IDEALIST project, H2020 METRO-HAUL) as well as in several research grant projects and technology transfer activities. He has co-authored 5 book chapters, over 200 international and peer-reviewed journal and conference papers and 5 Internet Engineering Task Force (IETF) Request For Comments (RFCs). He has served as TPC member of conferences related to optical networks such as ECOC or OFC and he is Associate Editor of JOCN.
Title: SDN Control of Disaggregated optical Networks with OpenConfig, OpenROADM
Abstract: Most deployed optical transport networks are proprietary, behaving as a closed, highly coupled, single-vendor managed domain. Although their control planes and management systems may export high-level and open northbound interfaces (NBI), the internal details and interfaces are not usually disclosed to the network operator. Driven by the requirements of telecommunication and data-center operators and the need to keep costs down while supporting sustained traffic increase, a trend known as aggregation has steadily emerged during the past years. Optical Disaggregation involves composing and assembling open and available components, devices and sub-systems into optical infrastructures and networks, combining “best-in-class” devices, tailored to the specific needs of the aforementioned operators. Disaggregation has been motivated by factors such as an increase in hardware commoditization, a perceived different rate of innovation of the different components, a promised acceleration in service deployment, or the consequent reduction in operational and capacity expenses. In practice, disaggregation brings multiple challenges, depending on the level that applies (e.g., partial or total, down to each of the optical components) and is taking place in stages. It is commonly accepted that disaggregation implies a trade-off between i) the opportunities due to the new degree of flexibility provided by component migration and upgrades without vendor lock-in, and ii) the potential decrease in performance compared to fully integrated systems and the underlying complexity – including interoperability requirements–, critical in full disaggregation scenarios. From the point of view of control and management, disaggregation heavily relies on the adoption of open interfaces exporting hardware programmability. Disaggregated optical networks are presented as an important use case for the adoption or a unified, model-driven development. In this paper, tutorial in nature, we will introduce the main concepts behind Software Defined Networking (SDN) for disaggregated optical networks, presenting reference architectures and industry common practices related to the adoption of a unified, model driven approach. This includes the use of the Yang data modelling language and the NETCONF/RESTCONF protocols, covering a subset of deployment models in partial and full disaggregation. The second part will cover an overview of selected deployment models (e.g., addressing transceiver and OLS disaggregation) as well as the OpenConfig and OpenROADM optical device models and Transport API (TAPI) interfaces, which constitute the main elements of the implemented SDN control plane. Such control plane targets mainly the metro segment, as defined within the EC Metro-Haul and ONF ODTN projects.
Manos Varvarigos, National Technical Univeristy of Athens, Greece
Emmanouel (Manos) Varvarigos received a Diploma in Electrical and Computer Engineering from the National Technical University of Athens (NTUA) in 1988, and the M.S. and Ph.D. degrees in Electrical Engineering and Computer Science from the Massachusetts Institute of Technology (MIT) in 1990 and 1992, respectively. He has held faculty positions at the University of California, Santa Barbara (1992-1998, ECE dept, as an Assistant and later an Associate Professor), Delft University of Technology, the Netherlands (1998-2000, ECE dept, as an Associate Professor), University of Patras, Greece (2000-June 2015, CEID, as a Professor), and National Technical University of Athens (June 2015-now, ECE dept, as a Professor). During 2017 – 2018 he was also a Professor and Head of the Electrical and Computer Systems Engineering Department at Monash University, on leave of absence from NTUA. From 2003-2016, he was the Scientific Director of the Greek School Network and Network Technologies Division of the Computer Technology Institute “Diophantus” (CTI), which through its involvement in pioneering research and development projects, has a major role in the development of network technologies and telematic services in Greece, and is responsible for development and operation of the Greek School Network, the largest public Network in Greece. He has participated in 30 USA-, Australian-, and EU- funded research projects in the areas of optical networking, optical interconnects, grid and cloud computing, and smart energy grids, being the consortium coordinator in 6 of them, and in many Greek research projects. He has also worked as a researcher at Bell Communications Research, and has consulted with several companies in the USA and in Europe. He has over 350 publications in refereed international journals and conferences. His research activities are in the areas of optical communication networks, optical interconnects for Data Centers, network protocols, grid and cloud computing, smart energy grids, 5G networks, and network services.
Title: Self-monitored, self-adjusted optical network
Abstract: Internet traffic is increasing due to new and evolving applications such as cloud computing, Internet of Things and the 5G networks. Many of these applications exhibit significant variation with time, putting further stress in the network’s infrastructure. Traditionally, optical networks are operated statically and are overprovisioned to account for future traffic increases and equipment performance deteriorations. This translates to additional installed equipment that increases the capital expenditure. Also, the network operator has to correlate a vast amount of information in order to manually manage the network resources. A self-monitored and self-adjusted optical network can provide the means to increase the efficiency, reduce the network costs and optimize its’ functions. A self-adjusted network continuously observes and analyzes its’ state and decides on the appropriate reconfiguration actions in order to adapt to current or future traffic demands, anticipate or recover from failures and optimize the efficiency. A centralized SDN-based network controller is responsible for the orchestration of the network functions and algorithms. A self-adjusted network leverages monitoring information (e.g Quality of Transmission metrics) from various sources (e.g. coherent receivers, power monitors). This information is analyzed using machine learning and big data algorithms. Specific insights are obtained regarding the physical layer conditions, the adequacy of network capacity with respect to current traffic demands and the expected Quality of Transmission of candidate lightpaths. Machine learning algorithms are also used to predict future traffic demands and to promptly anticipate and localize equipment failures. The self-adjusted network automatically adapts the rate or routing of existing lightpaths, establishes or tears down lightpaths, responds to network failures and anticipates future required equipment.