Tutorial Speakers
Dr. Haoran Chi
Instituto de Telecomunicacoes, Portugal
Haoran Chi is currently a Permanent Senior Researcher at the Instituto de Telecomunicações, Portugal. Prior to that, he worked as a research scholar at North Carolina State University from 2018 to 2019. He obtained his Ph.D. degree from the City University of Hong Kong in 2018. Throughout his career, Dr. Chi has gained expert knowledge in 5G (and beyond) telecommunications, network management, and machine learning. Dr. Chi has published more than 70 technical papers and has successfully coordinated and managed multiple European projects. He is the Work Package (WP) manager for the EU projects Exigence, Safe-home, Joconet, and Muscles, as well as for the FCT project 5G-AHEAD. He is also the Principal Investigator (PI) of the Portuguese institutional project NICE-HOME. Dr. Chi serves as the Deputy Editor-in-Chief (EiC) of IEEE TCE Letters and as an Associate Editor for IEEE Transactions on Consumer Electronics, IEEE Transactions on Industrial Informatics, and IEEE Communications Standards Magazine. Additionally, he is a Guest Editor for IEEE Transactions on Consumer Electronics, IEEE Transactions on Industrial Informatics, and IEEE Journal of Biomedical and Health Informatics, among others. Dr. Chi is the Secretary and Liaison Leader of the IEEE IES Technical Committee on Building, Automation, Control, and Management. He also serves as the Vice Chair of the IEEE Standard P1451.5.5 Working Group and IEEE Standard P1451.5.6 Working Group.
Tutorial Title
Energy-Efficient Multi-Domain Network Orchestration and Management: Current Developments, Challenges, and Future Trends
Tutorial Abstract
This tutorial primarily introduces the current developments and future trends in energy-efficient multi-domain network orchestration and management (MD-OAM). It covers the ongoing energy efficient development of MD-OAM, including current standardization efforts (e.g., ETSI, 3GPP, ITU-T) and cutting-edge algorithms (e.g., federated learning). In particular, inspired by ongoing European projects (e.g., Exigence), this tutorial provides an overview of the latest demands from both academia and industry to achieve low-cost and decarbonized MD-OAM. To offer insight into how these demands can be addressed, the tutorial also includes key technological and algorithmic perspectives, with examples focused on inter-domain interoperability (full distribution of edge computing-based MD-OAM algorithms) and sustainability (energy-efficient deep reinforcement and federated learning-based MD-OAM).
