Abstract

In this keynote we will examine a phenomenon that can resignify communication networks as we know them. Instead of acting just as a data transport medium, multiple technologies have made it possible to leverage networks to run and provide user-level services. This paradigm has been alternately called Computing In the Network (COIN) and In-Network Computing (INC). INC has been mostly used in the context of programmable hardware, which provides support for the implementation of services on the data-layer level. Network Functions Virtualization (NFV) is another alternative technology to deploy novel types of services within the network. NFV allows the implementation in software of middleboxes traditionally available as specialized hardware. Network services can be implemented as SFCs (Service Function Chains) based on virtualization technologies that run on commodity hardware. Although most virtualized functions have classic middlebox functionalities (e.g. firewalls or intrusion detectors) arbitrary COIN services can be implemented using NFV technologies, which we call NFV-COIN. An NFV-COIN architecture has been proposed and published as an IETF Draft. We present case studies of NFV-COIN services for distributed abstractions that are notoriously relevant and hard to implement and maintain, including consensus, reliable and ordered broadcast, and failure detectors.

Biography

Elias P. Duarte Jr. is a Full Professor at Federal University of Parana, Curitiba, Brazil, where he is the leader of the Computer Networks, Distributed Systems & Security Lab (LaRSiS). He has been twice (2005 and 2009) Visiting Associate Professor at Tohoku University (Japan) and Visiting Researcher at the University of California at Irvine (1997). His research interests include Computer Networks and Distributed Systems, their Dependability, Management, and Algorithms. He has published over 300 peer-reviewer papers and has supervised more than 130 students both on the graduate and undergraduate levels. Prof. Duarte has been Associate Editor of the Computing (Springer) journal and IEEE Transactions on Dependable and Secure Computing, and has served as chair of 30 conferences and workshops in his fields of interest, including chairing TCPs of SRDS'18, ICDCS'21, and GLOBECOM'24. He received a Ph.D. degree in Computer Science from Tokyo Institute of Technology, Japan, 1997, M.Sc. degree in Telecommunications from the Polytechnical University of Madrid, Spain, 1991, and both BSc and MSc degrees in Computer Science from Federal University of Minas Gerais, Brazil, 1987 and 1991. He chaired the Brazilian National Laboratory on Computer Networks (2012-2016). He is a member of the IFIP WG 10.4 on Dependable Computing & Fault Tolerance, member of the Brazilian Computing Society (SBC) and a Senior Member of the IEEE.

Abstract

We have witnessed the explosive technology advancements in AI for last several years. As AI based Apps and services are being deployed extensively, and AI and IT infrastructure are tightly coupled. At the beginning, network infrastructure was used as an enabling vehicle for federated learning, which allows the training data decentralized to ensure the privacy of collaborating participants. Furthermore, for expedited and smooth provision of AI services, communication industries and academia are re-designing a novel network architecture where AI functionalities are distributed to end-devices such as RAN (Radio Access Node).

On the other hand, AI also promote the advancement of network technology. Traditional ML (Machine Learning) functions such as prediction and classification are successfully applied to a plethora of network related tasks including traffic classification, anomaly detection, and bandwidth prediction. In addition to prediction and classification, DRL (Deep Reinforcement Learning) is used for complicated network control protocols such as congestion control, ABR(Adaptive Bit Rate) streaming and CJS (cloud cluster job scheduling). Finally, LLMs (Large Language Models) and LMMs (Large Multi-modal Models), based on the transformer or self-attention architecture, are expeditiously emerging as a network engineering platform. Recent studies demonstrate the prospect of these techniques as a generalized intelligence for optimization of multiple network tasks.

Biography

Dr. Chong-Kwon Kim received BS degree and MS degrees in Industrial Engineering from the Seoul National University and the Georgia Institute of Technology, respectively. He then switched his specialties and received his Ph.D degree in Computer Science from the University of Illinois at Urbana-Champaign. He is currently a Distinguished Professor at Korea Institute of Energy Technology (KENTECH). He is serving on a Director of Research Institute of Energy AI at KENTECH, focusing on the integration of AI technology into various problems in energy area. Before KENTECH, he has worked as an MTS at Bell Communications Research from 1987 to 1991 and as a professor at the Seoul National University from 1991 to 2021. During his 30 plus more career, he published more than 100 research papers in the field of computer network, performance analysis, machine learning, data mining and machine learning. He has served on a president of Korea Institute of Information Scientists and Engineers during 2014 and 2015 and has been serving on steering committee of several international conferences. His research interests include machine learning and its applications to energy systems.

Abstract

The RAN intelligent controller (RIC) is cloud native and a central component of an open and virtualized RAN network. The RIC enables to deployment of machine learning and AI techniques to optimize resources and services in the RAN. Thus, it is an important component that brings intelligence, agility, and programmability to the radio access network. On the other hand, semantic communication has emerged as a new communication paradigm that aims at the successful transmission of semantic information conveyed by the transmitter rather than the accurate reception of each single bit regardless of the meaning. With semantic communication, it is likely that some form of intelligence is needed at the RAN to enable such a paradigm. In this talk, we will share how RIC is going to enable semantic communications to become reality. Furthermore, we will also share some initial work in this area through Singapore’s first national Future Communications Research and Development Programme (FCP).

Biography

Tony Q. S. Quek received the B.E. and M.E. degrees in Electrical and Electronics Engineering from Tokyo Institute of Technology, respectively. At Massachusetts Institute of Technology, he earned the Ph.D. in Electrical Engineering and Computer Science. Currently, he is the Cheng Tsang Man Chair Professor with Singapore University of Technology and Design (SUTD) and ST Engineering Distinguished Professor. He also serves as the Head of ISTD Pillar, Director for Future Communications R&D Programme, Sector Lead for SUTD AI Program, and the Deputy Director of SUTD-ZJU IDEA. His current research topics include wireless communications and networking, 6G, network intelligence, non-terrestrial networks, and open radio access network.

Dr. Quek is currently serving as an Area Editor for the IEEE Transactions on Wireless Communications. He was an Executive Editorial Committee Member of the IEEE Transactions on Wireless Communications, an Editor of the IEEE Transactions on Communications, and an Editor of the IEEE Wireless Communications Letters. He received the 2008 Philip Yeo Prize for Outstanding Achievement in Research, the 2012 IEEE William R. Bennett Prize, the 2016 IEEE Signal Processing Society Young Author Best Paper Award, the 2017 CTTC Early Achievement Award, the 2017 IEEE ComSoc AP Outstanding Paper Award, the 2020 IEEE Communications Society Young Author Best Paper Award, the 2020 IEEE Stephen O. Rice Prize, the 2020 Nokia Visiting Professorship, and the the 2022 IEEE Signal Processing Society Best Paper Award. He is an IEEE Fellow, a WWRF Fellow, and a Fellow of the Academy of Engineering Singapore.