Keynotes

João Pedro

Title: Reliability Challenges and Opportunities in Next-Generation Optical Networks

Abstract: Optical networks have been an (often underestimated) enabler of our hyper-connected societies, as they fulfil the key role of reliably carrying massive amounts of data across medium to long distances. The relentless demand for traffic and the potential impact of massive AI workloads on inter data center traffic requirements, are setting the stage for even higher capacity needs. However, since we already ripped most of the spectral efficiency improvements of the “coherent revolution” that started back in 2010, this time around more capacity will mean more spectrum. Exploiting wideband transmission is an appealing approach to postpone costly roll out of new optical fibers, particularly for service providers that are fiber constrained. This talk will overview solutions to exploit more transmission bands, highlighting the associated technical challenges, with a specific focus on reliability, and discussing potential mitigation strategies. Moreover, it will also address recent developments on using machine learning techniques and real-time monitoring data for early failure detection and proactive restoration, which can play an important role in cost-effectively improving service availability in optical networks.

Short Bio: João Pedro holds a M.Sc. and Ph.D. degrees in Electrical and Computer Engineering from Instituto Superior Técnico (IST), University of Lisbon, Portugal. He was a research engineer and a system architect at Nokia Siemens Networks and Coriant and he is currently a senior principal engineer at Infinera, being responsible for the design of capacity planning algorithms for next-generation optical networks and supporting performance and techno-economic investigations of future-looking network architectures. He holds 13 patent applications and has co-authored over 300 publications in international conferences and journals. He has also participated in several EU-funded projects and has been a lecturer of courses on network planning and transport networks. His current research interests include high-capacity and reliable optical networks, flexible metro-aggregation architectures, routing and spectrum assignment, multilayer optimization and machine learning applications. He is also a permanent staff member of Instituto de Telecomunicações, a senior member of the IEEE and a member of OPTICA.

Kohei Shiomoto

Title: Data-driven network management

Abstract: Data-driven network management has been an active research filed for more than past 10 years. Mathematical methods such as machine learning enables data-driven network management. In particular, deep learning has developed rapidly, and it is being applied not only to image, audio, and natural language processing, but also to various industrial fields, and ICT system operation management is no exception. In this talk, after first explaining the architecture and management of modern computer networks, we will cover the mathematical methods that support data-driven network management, including deep learning and other machine learning methods. We will then cover the research trends related to data-driven management, including network softwarization and network security.

Short Bio: Kohei Shiomoto is a Professor, Tokyo City University, Tokyo Japan. Since joining NTT Laboratories in 1989, he has been engaged in research and development in the data communications industry on high-speed computer network architecture, traffic management, and network analysis to create innovative technologies for the Internet, mobile, and cloud computing. From 1996 to 1997, he was a visiting scholar at Washington University in St. Louis, MO, USA. In 2017, he joined Tokyo City University to engage in research and education on data science and computer networking. Current research interests include data mining for network management, human flow analysis, cloud computing and blockchain. He has published more than 70 academic papers, 130 refereed international conference papers, and 6 RFCs in IETF. He served as Guest Co-Editor for a series of special issues established in IEEE Transactions on Network and Service Management. He has served in various roles organizing IEEE ComSoc conferences including IEEE NOMS, IEEE IM, and IEEE NetSoft. He served as the lead Series editor for the Network Softwarization and Management Series in IEEE Communications Magazine, 2018-2021. He is a Fellow of the Institute of Electronics, Information and Communication Engineers (IEICE), a Senior Member of the IEEE, and a member of the ACM and the Information Processing Society of Japan (IPSJ).

Kui Ren

Title: Data Security: Challenges and Progresses

Short Bio: Kui Ren, Qiushi Chair Professor of Zhejiang University, AAAS, ACM, CCF, and IEEE Fellow, is currently the dean of the College of Computer Science and Technology of Zhejiang University and the executive deputy director of the State Key Laboratory of Blockchain and Data Security. Professor Kui Ren is mainly engaged in research in data security and privacy protection, AI security, and security in intelligent devices and vehicular networks. He has presided over major domestic and foreign scientific research projects such as the Ministry of Science and Technology, the National Natural Science Foundation of China, and the National Science Foundation of the United States. He has won the First Prize of Zhejiang Provincial Natural Science Award, the First Prize in Natural Science of the Chinese Institute of Electronics Science and Technology Award, the first Guohua Outstanding Scholar Award of Zhejiang University, and the CISTC Technology Achievement Award of the IEEE Communications Society. He has published over 400 peer-reviewed journal and conference articles, with an H-Index of 97 and more than 52,000 citations.

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