Benchmarking and Profiling 5G Verticals' Applications: An Industrial IoT Use Case

Zafeiropoulos, Anastasios; Fotopoulou, Eleni; Peuster, Manuel; Schneider, Stefan; Gouvas, Panagiotis; Behnke, Daniel; Müller, Marcel; Bök, Patrick-Benjamin; Trakadas, Panagiotis; Karkazis, Panagiotis; Karl, Holger

doi:10.1109/netsoft48620.2020.9165393

Cited by 27 publications

(18 citation statements)

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“…The architecture for network service management and orchestration has been investigated in various research projects, for example the NGPaaS project (http://ngpaas.eu (accessed on 1 February 2021)) and the 5GTANGO project (https://www.5gtango.eu/ (accessed on 1 February 2021)), both sponsored by the 5G PPP research initiative. Numerous results of academic research on applying ML/analytics techniques for network management have also been published in the literature, for example [45][46][47].…”

Section: Open Issues and Opportunities For Future Researchmentioning

confidence: 99%

Intelligent and Autonomous Management in Cloud-Native Future Networks—A Survey on Related Standards from an Architectural Perspective

Duan

2021

Future Internet

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Cloud-native network design, which leverages network virtualization and softwarization together with the service-oriented architectural principle, is transforming communication networks to a versatile platform for converged network-cloud/edge service provisioning. Intelligent and autonomous management is one of the most challenging issues in cloud-native future networks, and a wide range of machine learning (ML)-based technologies have been proposed for addressing different aspects of the management challenge. It becomes critical that the various management technologies are applied on the foundation of a consistent architectural framework with a holistic vision. This calls for standardization of new management architecture that supports seamless the integration of diverse ML-based technologies in cloud-native future networks. The goal of this paper is to provide a big picture of the recent developments of architectural frameworks for intelligent and autonomous management for future networks. The paper surveys the latest progress in the standardization of network management architectures including works by 3GPP, ETSI, and ITU-Tand analyzes how cloud-native network design may facilitate the architecture development for addressing management challenges. Open issues related to intelligent and autonomous management in cloud-native future networks are also discussed in this paper to identify some possible directions for future research and development.

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Section: Open Issues and Opportunities For Future Researchmentioning

confidence: 99%

Intelligent and Autonomous Management in Cloud-Native Future Networks—A Survey on Related Standards from an Architectural Perspective

Duan

2021

Future Internet

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“…It can significantly reduce the country to a share as the return of total traffic, which is essential in the future 34 . Researchers predict ExaBytes data for the month until 2025 for connected vehicles only 35 From a research study, knowledge about the analysis of information and the development of responses to or near the website is obtained by data and subsequent 64 . Thus, knowledge can reduce waiting time, cost, and security, thus increasing the business's efficiency.…”

Section: Multi-access Edge Computing Use Casesmentioning

confidence: 99%

Artificial Intelligence as a Service in Distributed Multi Access Edge Computing on 5g Extracting Data Using Iot and Including Ar/Vr for Real-Time Reporting

Madduru¹

2021

ITII

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To meet the growing demand for mobile data traffic and the stringent requirements for Internet of Things (IoT) applications in emerging cities such as smart cities, healthcare, augmented / virtual reality (AR / VR), fifth-generation assistive technologies generation (5G) Suggest and use on the web. As a major emerging 5G technology and a major driver of the Internet of Things, Multiple Access Edge Computing (MEC), which integrates telecommunications and IT services, provides cloud computing capabilities at the edge of an access network. wireless (RAN). By providing maximum compute and storage resources, MEC can reduce end-user latency. Therefore, in this article we will take a closer look at 5G MEC and the Internet of Things. Analyze the main functions of MEC in 5G and IoT environments. It offers several core technologies that enable the use of MEC in 5G and IoT, such as cloud computing, SDN / NFV, information-oriented networks, virtual machines (VMs) and containers, smart devices, shared networks and computing offload. This article also provides an overview of MEC's role in 5G and IoT, a detailed introduction to MEC-enabled 5G and IoT applications, and future perspectives for MEC integration with 5G and IoT. Additionally, this article will take a closer look at the MEC research challenges and unresolved issues around 5G and the Internet of Things. Finally, we propose a use case that MEC uses to obtain advanced intelligence in IoT scenarios.

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“…Para tanto é necessária a criação de perfis verticais acurados em termos de uso de recursos, eficiência elástica e capacidade de se adaptar dinamicamente às condições da rede. O uso dos perfis é fundamental para automatizar os processos de produção e desenvolvimento de software de automação sobre uma infraestrutura 5G [Zafeiropoulos et al, 2020]. Novas tecnologias precisam ser testadas e combinadas com validações práticas.…”

Section: A Privacidade Dos Dados Nas Redes 5gunclassified

Segurança em Redes 5G: Oportunidades e Desafios em Detecção de Anomalias e Predição de Tráfego Baseadas em Aprendizado de Máquina

Barbosa¹,

Bezerra²,

Medeiros³

et al. 2021

Minicursos Do XXI Simpósio Brasileiro De Segurança Da Informação E De Sistemas Computacionais

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This chapter focuses on approaching and contextualizing the security of the fifthgeneration (5G) mobile networks, discussing network anomaly detection techniques through hybrid tools. Classical techniques for prediction, such as time series regression analysis and the Hidden Markov Model, are revisited. New anomaly detection and traffic prediction techniques based on deep learning are presented, such as recurrent neural networks, neural networks with long short-term memory, and convolutional neural networks. Finally, the challenges and new paradigms of the next-generation networks (6G) are presented. We also present a case study with a practical exercise to develop an example of anomaly detection and traffic prediction through open source and free tools. ResumoEste capítulo aborda e contextualiza a segurança das redes móveis de quinta geração (5G), discutindo técnicas de predição de tráfego e detecção de anomalias em redes através de ferramentas híbridas. Revisitam-se técnicas clássicas para predição de tráfego, como análise de regressão de séries temporais e Modelo Oculto de Markov. Novas técnicas de detecção de anomalia e predição de tráfego baseadas em aprendizado profundo são apresentadas, tais como redes neurais recorrentes, redes neurais com memória longa de curto prazo e redes neurais convolucionais. Por fim, são apresentados os desafios da próxima geração de rede móveis (6G), novos paradigmas e um estudo de caso com exercício prático de desenvolvimento de um exemplo de detecção de anomalia e predição de tráfego através de ferramentas livres de código aberto.

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Benchmarking and Profiling 5G Verticals' Applications: An Industrial IoT Use Case

Cited by 27 publications

References 18 publications

Intelligent and Autonomous Management in Cloud-Native Future Networks—A Survey on Related Standards from an Architectural Perspective

Intelligent and Autonomous Management in Cloud-Native Future Networks—A Survey on Related Standards from an Architectural Perspective

Artificial Intelligence as a Service in Distributed Multi Access Edge Computing on 5g Extracting Data Using Iot and Including Ar/Vr for Real-Time Reporting

Segurança em Redes 5G: Oportunidades e Desafios em Detecção de Anomalias e Predição de Tráfego Baseadas em Aprendizado de Máquina

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