5G-COMPLETE: Service-Driven Slice Management over Heterogeneous 5G Infrastructures

被引:3
|
作者
Moscatelli, Francesca [1 ]
Landi, Giada [1 ]
Mesodiakaki, Agapi [2 ]
Gatzianas, Marios [2 ]
Kalfas, George [2 ]
Tzanakaki, Anna [3 ]
Anastasopoulos, Markos [3 ]
Giannoulis, Giannis [4 ]
Tokas, Konstantinos [4 ]
Apostolopoulos, Dimitris [4 ]
Avramopoulos, Hercules [4 ]
Simeonidou, Dimitra [5 ]
Nejabati, Reza [5 ]
机构
[1] Nextworks, Pisa, Italy
[2] Aristotle Univ Thessaloniki, Ctr Interdisciplinary Res & Innovat, Dept Informat, Thessaloniki, Greece
[3] Natl & Kapodistrian Univ Athens, Athens, Greece
[4] Natl Tech Univ Athens, Inst Commun & Comp Syst, Photon Commun Res Lab, Athens, Greece
[5] Univ Bristol, Bristol, Avon, England
关键词
Edge-cloud; management and orchestration; 5G networks; end-to-end network slicing; network optimization; RESOURCE-ALLOCATION; NETWORKS;
D O I
10.1109/CAMAD52502.2021.9617774
中图分类号
TP3 [计算技术、计算机技术];
学科分类号
0812 ;
摘要
In this paper, we present the 5G-COMPLETE project, which focuses on the delivery of novel technological blocks in both physical connectivity and network softwarization. In particular, the 5G-COMPLETE solution employs service-driven slice management in 5G and beyond 5G (B5G) networks, while integrating the orchestration of radio, transport and core network resources as well as cloud and edge computing resources. 5G-COMPLETE aims to build upon a well-defined architecture as well as the network management and orchestration layer with two main goals: i) develop novel networking and compute blocks, integrating them in a heterogeneous infrastructure where service-driven slice management can assist use cases in realistic deployment options, ii) develop solutions to provide a higher and more intelligent level of network management boosted by Artificial Intelligence/Machine Learning (AI/ML) assisted tools for improving operational efficiency, energy efficiency and reducing the operational expenditure of 5G and B5G networks.
引用
收藏
页数:6
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