NASOR: A network slicing approach for multiple Autonomous Systems

Moreira, Rodrigo; Rosa, Pedro Frosi; Aguiar, Rui L.; Silva, Flávio de Oliveira

doi:10.1016/j.comcom.2021.07.028

Cited by 8 publications

(3 citation statements)

References 40 publications

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“…The network slicing surveys in [23] [3] [18] [7] and the network slicing architectures presented in [1], [6], [8], [10], [11], [12], and [14] further detail the existing architectural features orchestration for NS. In Table 1, security orchestration refers to the ability to orchestrate security functionalities not only for slices but also for the architecture that provides these slices.…”

Section: Architectural Feature Sfi2 3gpp Itu-t Etsi Ietf Necos Nasormentioning

confidence: 99%

Intelligent Data-Driven Architectural Features Orchestration for Network Slicing

Martins,

Moreira,

Silva

et al. 2024

Preprint

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Network slicing is a crucial enabler and a trend for the Next Generation Mobile Network (NGMN) and various other new systems like the Internet of Vehicles (IoV) and Industrial IoT (IIoT). Orchestration and machine learning are key elements with a crucial role in the network-slicing processes since the NS process needs to orchestrate resources and functionalities, and machine learning can potentially optimize the orchestration process. However, existing network-slicing architectures lack the ability to define intelligent approaches to orchestrate features and resources in the slicing process. This paper discusses machine learning-based orchestration of features and capabilities in network slicing architectures. Initially, the slice resource orchestration and allocation in the slicing planning, configuration, commissioning, and operation phases are analyzed. In sequence, we highlight the need for optimized architectural feature orchestration and recommend using ML-embed agents, federated learning intrinsic mechanisms for knowledge acquisition, and a data-driven approach embedded in the network slicing architecture. We further develop an architectural features orchestration case embedded in the SFI2 network slicing architecture. An attack prevention security mechanism is developed for the SFI2 architecture using distributed embedded and cooperating ML agents. The case presented illustrates the architectural feature’s orchestration process and benefits, highlighting its importance for the network slicing process.

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Section: Architectural Feature Sfi2 3gpp Itu-t Etsi Ietf Necos Nasormentioning

confidence: 99%

Intelligent Data-Driven Architectural Features Orchestration for Network Slicing

Martins,

Moreira,

Silva

et al. 2024

Preprint

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“…Among such projects, 5Growth has used artificial intelligence and machine learning solutions to enhance service automation in network slices. Also, Moreira et al [35] designed and evaluated the NASOR, an orchestration concept towards multi-domain network slicing on top of Internet routers.…”

Section: B Network Slicing Projectsmentioning

confidence: 99%

Enhancing Network Slicing Architectures With Machine Learning, Security, Sustainability and Experimental Networks Integration

Martins

Carvalho²,

Moreira³

et al. 2023

IEEE Access

Self Cite

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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 endto-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.

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“…Different state-of-the-art approaches made efforts to support multi-tenant network sharing and open up business opportunities across various verticals. These initiatives focused on building and evaluating architectures capable of deploying network slices in different domains, especially using heterogeneous technologies [Moreira et al 2021]. Artificial Intelligence (AI) has played an important role in improving various aspects of the network slice lifecycle with security and reliability.…”

Section: Introductionmentioning

confidence: 99%

Designing and Evaluating a high-reliable and security-aware Identity and Access Management for Slicing Architectures

Moreira¹,

Carvalho

Silva

2023

Anais Do XIV Workshop De Pesquisa Experimental Da Internet Do Futuro (WPEIF 2023)

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Network slicing architectures are fundamental for providing connectivity to demanding users and applications in heterogeneous network infrastructures. Such architectures have evolved significantly in recent years, especially with improvements in security and reliability functions. However, the improvements in these architectures are functionally specific and are not considered throughout the entire architecture lifecycle, opening opportunities for secure, and reliable native architectures. Thus, this paper designs and evaluates an Identity and Access Management (IAM) mechanism while providing security and reliability for building blocks of slicing architectures. Our findings concern a comparative evaluation of the IAM mechanism and its behavior under stress loads, as well as an experimental assessment of a secure defense mechanism against Distributed Denial-of-Service (DDoS) attacks.

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NASOR: A network slicing approach for multiple Autonomous Systems

Cited by 8 publications

References 40 publications

Intelligent Data-Driven Architectural Features Orchestration for Network Slicing

Intelligent Data-Driven Architectural Features Orchestration for Network Slicing

Enhancing Network Slicing Architectures With Machine Learning, Security, Sustainability and Experimental Networks Integration

Designing and Evaluating a high-reliable and security-aware Identity and Access Management for Slicing Architectures

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