TY - CHAP
T1 - An Evolutionary Edge Computing Architecture for the Beyond 5G Era
AU - Kartsakli, Elli
AU - Perez-Romero, Jordi
AU - Bartzoudis, Nikolaos
AU - Sallent, Oriol
AU - Kolawole, Oluwatayo
AU - Tao, Xin
AU - Mohalik, Swarup Kumar
AU - Mach, Tomasz
AU - Liu, Sige
AU - Deng, Yansha
AU - Mando, Gianluca
AU - Antonopoulos, Angelos
AU - Frascolla, Valerio
AU - Kosu, Semiha
AU - Kalem, Gokhan
AU - Buining, Fred
AU - Quinones, Eduardo
N1 - Publisher Copyright:
© 2023 IEEE.
PY - 2023
Y1 - 2023
N2 - Beyond 5G (B5G) communication networks face the challenge of meeting the demanding requirements of various service types, including uRLLC, mIoT, eMBB, and emerging technologies like Extended Reality (XR). Edge computing can address these demands effectively because of the ability to bring computational power and resources closer to the source of data. Nevertheless, the realization of this potential necessitates an open, flexible, and automated architectural framework capable of supporting disaggregated applications and network designs. In this context, this paper introduces a novel architecture designed to advance the evolution of edge computing in B5G, developed within the EU-funded project VERGE. The proposed architecture is modular and scalable, guided by artificial intelligence (AI), and founded on three essential pillars: 'edge for AI,' 'AI for edge,' and 'security, privacy, and trustworthiness for AI.' After presenting this architecture, the paper showcases its applicability through the examination of two vertical use cases within the industrial and transportation domains.
AB - Beyond 5G (B5G) communication networks face the challenge of meeting the demanding requirements of various service types, including uRLLC, mIoT, eMBB, and emerging technologies like Extended Reality (XR). Edge computing can address these demands effectively because of the ability to bring computational power and resources closer to the source of data. Nevertheless, the realization of this potential necessitates an open, flexible, and automated architectural framework capable of supporting disaggregated applications and network designs. In this context, this paper introduces a novel architecture designed to advance the evolution of edge computing in B5G, developed within the EU-funded project VERGE. The proposed architecture is modular and scalable, guided by artificial intelligence (AI), and founded on three essential pillars: 'edge for AI,' 'AI for edge,' and 'security, privacy, and trustworthiness for AI.' After presenting this architecture, the paper showcases its applicability through the examination of two vertical use cases within the industrial and transportation domains.
KW - AI/ML-based optimization
KW - B5G/6G evolution
KW - closed-loop automation
KW - Edge computing
KW - edge-cloud compute continuum
KW - security and trustworthiness
UR - http://www.scopus.com/inward/record.url?scp=85190508557&partnerID=8YFLogxK
U2 - 10.1109/CAMAD59638.2023.10478426
DO - 10.1109/CAMAD59638.2023.10478426
M3 - Conference paper
AN - SCOPUS:85190508557
T3 - IEEE International Workshop on Computer Aided Modeling and Design of Communication Links and Networks, CAMAD
SP - 61
EP - 67
BT - 2023 IEEE 28th International Workshop on Computer Aided Modeling and Design of Communication Links and Networks, CAMAD 2023
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2023 IEEE 28th International Workshop on Computer Aided Modeling and Design of Communication Links and Networks, CAMAD 2023
Y2 - 6 November 2023 through 8 November 2023
ER -