Empowering Beyond 5G Networks: An Experimental Assessment of Zero-Touch Management and Orchestration

被引:0
作者
Barrachina-Munoz, Sergio [1 ]
Rezazadeh, Farhad [1 ]
Blanco, Luis [1 ]
Kuklinski, Slawomir [2 ,3 ]
Zeydan, Engin [1 ]
Chawla, Ashima [4 ]
Zanzi, Lanfranco [5 ]
Devoti, Francesco [5 ]
Vlahodimitropoulou, Vasiliki [6 ]
Chochliouros, Ioannis [6 ]
Bosneag, Anne-Marie [4 ]
Cherrared, Sihem [7 ]
Vettori, Luca [1 ]
Mangues-Bafalluy, Josep [1 ]
机构
[1] Ctr Tecnol Telecomunicac Catalunya CTTC, Barcelona 08860, Spain
[2] Orange Polska, PL-02326 Warsaw, Poland
[3] Warsaw Univ Technol, Elect & Informat Technol, PL-00661 Warsaw, Poland
[4] Ericsson Ireland, Athlone N37 PV44, Albania
[5] NEC Labs Europe, D-69115 Heidelberg, Germany
[6] Hellen Telecommun Org SA OTE, Athens 15124, Greece
[7] Orange France, F-92320 Chatillon, France
基金
欧盟地平线“2020”;
关键词
5G mobile communication; 6G mobile communication; Scalability; Monitoring; Resource management; Network slicing; Federated learning; Cloud computing; Automation; Anomaly detection; scalability; network management; orchestration; B5G; testbed; DRIVEN; INTELLIGENCE; FRAMEWORK; RAN;
D O I
10.1109/ACCESS.2024.3510804
中图分类号
TP [自动化技术、计算机技术];
学科分类号
0812 ;
摘要
Effective zero-touch management and orchestration (ZSM&O) is crucial for scaling network slicing, particularly transitioning toward Beyond 5G (B5G) and 6G networks. This paper empirically validates the network slicing framework developed under the European Union Horizon 2020 MonB5G project. Building on three years of academia-industry collaboration, MonB5G introduces a flexible slicing model featuring umbrella slices that orchestrate modular, specialized slices across multi-domain environments to address next-generation service demands. For the first time, we evaluate its practicality in a 5G cloud-native testbed through a virtual reality (VR) streaming use case, supported by solutions such as federated learning-based CPU forecasting, anomaly detection, and deep reinforcement learning (DRL) for radio access network (RAN) optimization. The paper offers insights from technically demanding experimental tests and highlights challenges and development paths for managing next-generation mobile networks.
引用
收藏
页码:182752 / 182762
页数:11
相关论文
共 35 条
[1]   Ensemble Learning-based Network Data Analytics for Network Slice Orchestration and Management: An Intent-Based Networking Mechanism [J].
Abbas, Khizar ;
Khan, Talha Ahmed ;
Afaq, Muhammad ;
Song, Wang-Cheol .
PROCEEDINGS OF THE IEEE/IFIP NETWORK OPERATIONS AND MANAGEMENT SYMPOSIUM 2022, 2022,
[2]   Dynamic Resource Provisioning of a Scalable E2E Network Slicing Orchestration System [J].
Afolabi, Ibrahim ;
Prados-Garzon, Jonathan ;
Bagaa, Miloud ;
Taleb, Tarik ;
Ameigeiras, Pablo .
IEEE TRANSACTIONS ON MOBILE COMPUTING, 2020, 19 (11) :2594-2608
[3]  
[Anonymous], 2021, document TS 23.288
[4]   Cloud Native Federated Learning for Streaming: An Experimental Demonstrator [J].
Barrachina-Munoz, Sergio ;
Zeydan, Engin ;
Blanco, Luis ;
Vettori, Luca ;
Rezazadeh, Farhad ;
Mangues-Bafalluy, Josep .
2023 IEEE 24TH INTERNATIONAL CONFERENCE ON HIGH PERFORMANCE SWITCHING AND ROUTING, HPSR, 2023,
[5]   Deploying cloud-native experimental platforms for zero-touch management 5G and beyond networks [J].
Barrachina-Munoz, Sergio ;
Nikbakht, Rasoul ;
Baranda, Jorge ;
Payaro, Miquel ;
Mangues-Bafalluy, Josep ;
Kokkinos, Panagiotis ;
Soumplis, Polyzois ;
Kretsis, Aristotelis ;
Varvarigos, Emmanouel .
IET NETWORKS, 2023, 12 (06) :305-315
[6]   Network Slicing Meets Artificial Intelligence: An AI-Based Framework for Slice Management [J].
Bega, Dario ;
Gramaglia, Marco ;
Garcia-Saavedra, Andres ;
Fiore, Marco ;
Banchs, Albert ;
Costa-Perez, Xavier .
IEEE COMMUNICATIONS MAGAZINE, 2020, 58 (06) :32-38
[7]   AI-Driven Framework for Scalable Management of Network Slices [J].
Blanco, Luis ;
Kuklinski, Slawomir ;
Zeydan, Engin ;
Rezazadeh, Farhad ;
Chawla, Ashima ;
Zanzi, Lanfranco ;
Devoti, Francesco ;
Kolakowski, Robert ;
Vlahodimitropoulou, Vasiliki ;
Chochliouros, Ioannis ;
Bosneag, Anne-Marie ;
Cherrared, Sihem ;
Garrido, Luis A. ;
Barrachina-Munoz, Sergio ;
Mangues, Josep .
IEEE COMMUNICATIONS MAGAZINE, 2023, 61 (11) :216-222
[8]   Intelligence and Learning in O-RAN for Data-Driven NextG Cellular Networks [J].
Bonati, Leonardo ;
D'Oro, Salvatore ;
Polese, Michele ;
Basagni, Stefano ;
Melodia, Tommaso .
IEEE COMMUNICATIONS MAGAZINE, 2021, 59 (10) :21-27
[9]   Dynamic slicing reconfiguration for virtualized 5G networks using ML forecasting of computing capacity [J].
Camargo, Juan Sebastian ;
Coronado, Estefania ;
Ramirez, Wilson ;
Camps, Daniel ;
Deutsch, Sergi Sanchez ;
Perez-Romero, Jordi ;
Antonopoulos, Angelos ;
Trullols-Cruces, Oscar ;
Gonzalez-Diaz, Sergio ;
Otura, Borja ;
Rigazzi, Giovanni .
COMPUTER NETWORKS, 2023, 236
[10]  
Chawla A., 2023, P NOMS IEEE IFIP NET, P1