Enhancing Network Slicing Architectures With Machine Learning, Security, Sustainability and Experimental Networks Integration
Network Slicing (NS) is an essential technique extensively used in 5G networks computing strategies, mobile edge computing, mobile cloud computing, and verticals like the Internet of Vehicles and industrial IoT, among others. NS is foreseen as one of the leading enablers for 6G futuristic and highly...
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IEEE
2023-01-01
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| Online Access: | https://ieeexplore.ieee.org/document/10173493/ |
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| author | Joberto S. B. Martins Tereza C. Carvalho Rodrigo Moreira Cristiano Bonato Both Adnei Donatti Joao H. Correa Jose A. Suruagy Sand L. Correa Antonio J. G. Abelem Moises R. N. Ribeiro Jose-Marcos S. Nogueira Luiz C. S. Magalhaes Juliano Wickboldt Tiago C. Ferreto Ricardo Mello Rafael Pasquini Marcos Schwarz Leobino N. Sampaio Daniel F. Macedo Jose F. De Rezende Kleber V. Cardoso Flavio De Oliveira Silva |
| author_facet | Joberto S. B. Martins Tereza C. Carvalho Rodrigo Moreira Cristiano Bonato Both Adnei Donatti Joao H. Correa Jose A. Suruagy Sand L. Correa Antonio J. G. Abelem Moises R. N. Ribeiro Jose-Marcos S. Nogueira Luiz C. S. Magalhaes Juliano Wickboldt Tiago C. Ferreto Ricardo Mello Rafael Pasquini Marcos Schwarz Leobino N. Sampaio Daniel F. Macedo Jose F. De Rezende Kleber V. Cardoso Flavio De Oliveira Silva |
| author_sort | Joberto S. B. Martins |
| collection | DOAJ |
| description | Network Slicing (NS) is an essential technique extensively used in 5G networks computing strategies, mobile edge computing, mobile cloud computing, and verticals like the Internet of Vehicles and industrial IoT, among others. NS is foreseen as one of the leading enablers for 6G futuristic and highly demanding applications since it allows the optimization and customization of scarce and disputed resources among dynamic, demanding clients with highly distinct application requirements. Various standardization organizations, like 3GPP’s proposal for new generation networks and state-of-the-art 5G/6G research projects, are proposing new NS architectures. However, new NS architectures have to deal with an extensive range of requirements that inherently result in having NS architecture proposals typically fulfilling the needs of specific sets of domains with commonalities. The Slicing Future Internet Infrastructures (SFI2) architecture proposal explores the gap resulting from the diversity of NS architectures target domains by proposing a new NS reference architecture with a defined focus on integrating experimental networks and enhancing the NS architecture with Machine Learning (ML) native optimizations, energy-efficient slicing, and slicing-tailored security functionalities. The SFI2 architectural main contribution includes the utilization of the slice-as-a-service paradigm for end-to-end orchestration of resources across multi-domains and multi-technology experimental networks. In addition, the SFI2 reference architecture instantiations will enhance the multi-domain and multi-technology integrated experimental network deployment with native ML optimization, energy-efficient aware slicing, and slicing-tailored security functionalities for the practical domain. |
| format | Article |
| id | doaj-art-0dcd0c86ff05406f9d12aa39c8a05151 |
| institution | Kabale University |
| issn | 2169-3536 |
| language | English |
| publishDate | 2023-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj-art-0dcd0c86ff05406f9d12aa39c8a051512025-08-20T03:58:06ZengIEEEIEEE Access2169-35362023-01-0111691446916310.1109/ACCESS.2023.329278810173493Enhancing Network Slicing Architectures With Machine Learning, Security, Sustainability and Experimental Networks IntegrationJoberto S. B. Martins0https://orcid.org/0000-0003-1310-9366Tereza C. Carvalho1https://orcid.org/0000-0002-0821-0614Rodrigo Moreira2https://orcid.org/0000-0002-9328-8618Cristiano Bonato Both3https://orcid.org/0000-0002-9776-4888Adnei Donatti4https://orcid.org/0000-0002-4085-9640Joao H. Correa5https://orcid.org/0000-0002-8124-8985Jose A. Suruagy6https://orcid.org/0000-0001-7157-5045Sand L. Correa7https://orcid.org/0000-0003-1863-4661Antonio J. G. Abelem8https://orcid.org/0000-0003-4085-6674Moises R. N. Ribeiro9https://orcid.org/0000-0002-9149-2391Jose-Marcos S. Nogueira10https://orcid.org/0000-0002-1095-6714Luiz C. S. Magalhaes11https://orcid.org/0000-0002-1651-3156Juliano Wickboldt12https://orcid.org/0000-0002-7686-8370Tiago C. Ferreto13https://orcid.org/0000-0001-8485-529XRicardo Mello14https://orcid.org/0000-0003-0420-4273Rafael Pasquini15https://orcid.org/0000-0002-8781-3914Marcos Schwarz16https://orcid.org/0000-0002-3461-3548Leobino N. Sampaio17https://orcid.org/0000-0003-4855-0936Daniel F. Macedo18https://orcid.org/0000-0001-6668-4175Jose F. De Rezende19https://orcid.org/0000-0002-5660-6488Kleber V. Cardoso20https://orcid.org/0000-0001-5152-5323Flavio De Oliveira Silva21https://orcid.org/0000-0001-7051-7396Computer Science Department, Universidade Salvador (UNIFACS), Salvador, BrazilComputer Engineering and Digital Systems Department, Universidade de São Paulo (USP), São Paulo, BrazilInstitute of Exact and Technological Sciences, Universidade Federal de Viçosa (UFV), Viçosa, BrazilUniversidade Federal do Vale dos Sinos (UNISINOS), PPGCA, Porto Alegre, BrazilComputer Engineering and Digital Systems Department, Universidade de São Paulo (USP), São Paulo, BrazilUniversidade Federal do Ceará (UFC), Fortaleza, BrazilInformatics Center, Universidade Federal de Pernambuco (UFPE), Recife, BrazilInstitute of Informatics, Universidade Federal de Goiás (UFG), Goiania, BrazilComputer Science Department, Universidade Federal do Pará (UFPA), Belém, BrazilElectrical Engineering Department, Universidade Federal do Espírito Santos (UFES), Vitória, BrazilComputer Science Department, Universidade Federal de Minas Gerais (UFMG), Belo Horizonte, BrazilTelecommunications Engineering Department, Universidade Federal Fluminense (UFF), Niteroi, BrazilInstitute of Informatics, Universidade Federal do Rio Grande do Sul (UFRGS), Porto Alegre, BrazilSchool of Technology, Pontifícia Universidade Católica do Rio Grande do Sul (PUCRS), Porto Alegre, BrazilElectrical Engineering Department, Universidade Federal do Espírito Santos (UFES), Vitória, BrazilFaculty of Computing, Universidade Federal de Uberlândia (UFU), Uberlândia, BrazilResearch, Development and Innovation Directory, Rede Nacional de Pesquisa (RNP), Rio de Janeiro, BrazilComputer Science Department, Universidade Federal da Bahia (UFBA), Salvador, BrazilComputer Science Department, Universidade Federal de Minas Gerais (UFMG), Belo Horizonte, BrazilSystems and Computer Engineering Program, Universidade Federal do Rio de Janeiro (UFRJ), Rio de Janeiro, BrazilInstitute of Informatics, Universidade Federal de Goiás (UFG), Goiania, BrazilFaculty of Computing, Universidade Federal de Uberlândia (UFU), Uberlândia, BrazilNetwork Slicing (NS) is an essential technique extensively used in 5G networks computing strategies, mobile edge computing, mobile cloud computing, and verticals like the Internet of Vehicles and industrial IoT, among others. NS is foreseen as one of the leading enablers for 6G futuristic and highly demanding applications since it allows the optimization and customization of scarce and disputed resources among dynamic, demanding clients with highly distinct application requirements. Various standardization organizations, like 3GPP’s proposal for new generation networks and state-of-the-art 5G/6G research projects, are proposing new NS architectures. However, new NS architectures have to deal with an extensive range of requirements that inherently result in having NS architecture proposals typically fulfilling the needs of specific sets of domains with commonalities. The Slicing Future Internet Infrastructures (SFI2) architecture proposal explores the gap resulting from the diversity of NS architectures target domains by proposing a new NS reference architecture with a defined focus on integrating experimental networks and enhancing the NS architecture with Machine Learning (ML) native optimizations, energy-efficient slicing, and slicing-tailored security functionalities. The SFI2 architectural main contribution includes the utilization of the slice-as-a-service paradigm for end-to-end orchestration of resources across multi-domains and multi-technology experimental networks. In addition, the SFI2 reference architecture instantiations will enhance the multi-domain and multi-technology integrated experimental network deployment with native ML optimization, energy-efficient aware slicing, and slicing-tailored security functionalities for the practical domain.https://ieeexplore.ieee.org/document/10173493/Network slicingnetwork slicing architectureexperimental networks integrationarchitectural slicing enhancementsML-native optimizationenergy-efficient slicing |
| spellingShingle | Joberto S. B. Martins Tereza C. Carvalho Rodrigo Moreira Cristiano Bonato Both Adnei Donatti Joao H. Correa Jose A. Suruagy Sand L. Correa Antonio J. G. Abelem Moises R. N. Ribeiro Jose-Marcos S. Nogueira Luiz C. S. Magalhaes Juliano Wickboldt Tiago C. Ferreto Ricardo Mello Rafael Pasquini Marcos Schwarz Leobino N. Sampaio Daniel F. Macedo Jose F. De Rezende Kleber V. Cardoso Flavio De Oliveira Silva Enhancing Network Slicing Architectures With Machine Learning, Security, Sustainability and Experimental Networks Integration IEEE Access Network slicing network slicing architecture experimental networks integration architectural slicing enhancements ML-native optimization energy-efficient slicing |
| title | Enhancing Network Slicing Architectures With Machine Learning, Security, Sustainability and Experimental Networks Integration |
| title_full | Enhancing Network Slicing Architectures With Machine Learning, Security, Sustainability and Experimental Networks Integration |
| title_fullStr | Enhancing Network Slicing Architectures With Machine Learning, Security, Sustainability and Experimental Networks Integration |
| title_full_unstemmed | Enhancing Network Slicing Architectures With Machine Learning, Security, Sustainability and Experimental Networks Integration |
| title_short | Enhancing Network Slicing Architectures With Machine Learning, Security, Sustainability and Experimental Networks Integration |
| title_sort | enhancing network slicing architectures with machine learning security sustainability and experimental networks integration |
| topic | Network slicing network slicing architecture experimental networks integration architectural slicing enhancements ML-native optimization energy-efficient slicing |
| url | https://ieeexplore.ieee.org/document/10173493/ |
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