@article{journal/access/MartinsSFI223,

title = {Enhancing Network Slicing Architectures With Machine Learning, Security, Sustainability and Experimental Networks Integration},

author = {Joberto Sérgio Barbosa Martins and Tereza Cristina Melo Brito Carvalho and Rodrigo Moreira and Cristiano Bonato Both and Adnei Donatti and João H. Corrêa and José Augusto Suruagy Monteiro and Sand Luz Corrêa and Antônio Jorge Gomes Abelém and Moisés Renato Nunes Ribeiro and José Marcos Silva Nogueira and Luiz Claudio Schara Magalhães and Juliano Araujo Wickboldt and Tiago Coelho Ferreto and Ricardo Mello and Rafael Pasquini and Marcos Schwarz and Leobino Nascimento Sampaio and Daniel Fernandes Macedo and José Ferreira Rezende and Kleber Vieira Cardoso and Flávio Oliveira Silva},

doi = {10.1109/ACCESS.2023.3292788},

year  = {2023},

date = {2023-07-05},

journal = {IEEE Access},

volume = {11},

pages = {69144-69163},

abstract = {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.},

keywords = {Experimental Approach, Machine Learning, Network Orchestration, Network Security, Network Virtualization & Slicing},

pubstate = {published},

tppubtype = {article}

}

@article{journals/jnsm/Martins5GMS23,

title = {Assisted Monitoring and Security Provisioning for 5G Microservices-Based Network Slices with SWEETEN},

author = {Rafael de Jesus Martins and Juliano Araujo Wickboldt and Lisandro Zambenedetti Granville},

url = {https://link.springer.com/article/10.1007/s10922-023-09728-1},

doi = {10.1007/s10922-023-09728-1},

issn = {1573-7705},

year  = {2023},

date = {2023-02-12},

urldate = {2023-02-12},

journal = {Journal of Network and Systems Management (JNSM)},

volume = {31},

number = {2},

pages = {36},

abstract = {5G networks have imposed a drastic shift in how mobile telecommunications must operate. In order to comply with the new requirements, solutions based on network function virtualization (NFV) and network slicing must be carried out. Regarding NFV in particular, the trend towards pulverizing the monolithic software in a microservices-based one carries network management challenges to operators. The deployment and integration of one or more network management software with the managed services is as important as it is delicate, as stringent requirements of 5G applications must be respected. In this paper, we propose SWEETEN as a solution for automating the deployment and transparently integrating network management solutions from different management disciplines, in this case, monitoring and security. Demonstrating its usability through a intelligent healthcare use case, SWEETEN is shown to transparently provide monitoring and security solutions for a complete network slice, enabling compliance with privacy requirements through minimal low-level interventions from the network slice tenant. The results show how SWEETEN integration of monitoring and security disciplines can assist users in guaranteeing the correct operation of their deployments regardless of the underlying software solutions used.},

keywords = {5G, Cloud Computing, Microservices, Network Orchestration, Network Security, Network Virtualization & Slicing},

pubstate = {published},

tppubtype = {article}

}