A Comparative Evaluation of Edge Cloud Virtualization Technologies

Valsamas, Polychronis; Mamatas, Lefteris; Contreras, Luis M.

doi:10.1109/tnsm.2021.3130792

Cited by 10 publications

(6 citation statements)

References 23 publications

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“…In addition to the aforementioned software optimization and acceleration, power allocation, access management, security, etc., NFV also faces research challenges such as the system complexity, cloud-native NFV-MANO [172], network programming and automation [166], service quality and data privacy in crowdsourced edge-based NFV [173], and edge cloud virtualization technologies [174]. There is still a long way to go to enhance and deploy NFV in real beyond 5G (B5G) and 6G networks.…”

Section: G Network Architecturementioning

confidence: 99%

On the Road to 6G: Visions, Requirements, Key Technologies, and Testbeds

Wang

You

Gao

et al. 2023

IEEE Commun. Surv. Tutorials

395

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Section: G Network Architecturementioning

confidence: 99%

On the Road to 6G: Visions, Requirements, Key Technologies, and Testbeds

Wang

You

Gao

et al. 2023

IEEE Commun. Surv. Tutorials

395

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“…On the other hand, from the infrastructure point of view, a variety of computing infrastructures will become available [21], offering a diversity of environments and managed in a non-homogeneous way. Different virtualization options can be suitable to distinct operational circumstances [22], as for example the availability of resources in a given moment.…”

Section: Cubic Conceptmentioning

confidence: 99%

“…the need for rapid deployment can be addressed by unikernels and robust service performance by containers). A relevant extensive performance evaluation along with particular architectural requirements, which we considered in the design of CUBIC, can be found in [22].…”

Section: Intelligent Utilization Of Compute Environmentsmentioning

confidence: 99%

Modular architecture providing convergent and ubiquitous intelligent connectivity for networks beyond 2030

Contreras¹,

Serrano²,

Mamatas³

et al. 2022

ITU J-FET

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The transition of the networks to support forthcoming beyond 5G (B5G) and 6G services introduces a number of important architectural challenges that force an evolution of existing operational frameworks. Current networks have introduced technical paradigms such as network virtualization, programmability and slicing, being a trend known as network softwarization. Forthcoming B5G and 6G services imposing stringent requirements will motivate a new radical change, augmenting those paradigms with the idea of smartness, pursuing an overall optimization on the usage of network and compute resources in a zero-trust environment. This paper presents a modular architecture under the concept of Convergent and UBiquitous Intelligent Connectivity (CUBIC), conceived to facilitate the aforementioned transition. CUBIC intends to investigate and innovate on the usage, combination and development of novel technologies to accompany the migration of existing networks towards Convergent and Ubiquitous Intelligent Connectivity (CUBIC) solutions, leveraging Artificial Intelligence (AI) mechanisms and Machine Learning (ML) tools in a totally secure environment.

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“…In practical monitoring systems, it is necessary to consider the complexity of computer software and hardware resources, as well as the time-delay characteristics of identification algorithms [7]. In the context of the Internet of Things, traditional cloud computing is insufficient in handling massive data, resulting in insufficient real-time capability, limited bandwidth, high energy consumption, and low data security [8,9].…”

Section: Introductionmentioning

confidence: 99%

Partial discharge diagnosis method for electrical equipment based on PSO-GRNN

Yang,

Song,

Zhai

et al. 2024

Eighth International Conference on Energy System, Electricity, and Power (ESEP 2023)

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Localized discharge refers to the phenomenon of insulation material discharge in electrical equipment under high electric field strength due to uneven distribution of electric field. The occurrence of localized discharge in equipment poses significant risks to the insulation layer, and a rapid and accurate identification of discharge types is crucial for the normal operation of industrial systems. In regard to the problem of identifying localized discharge types in electrical equipment, considering the timeliness and accuracy requirements of the monitoring system, a method is proposed that involves constructing a phase distribution spectrogram of localized discharge to extract statistical features. The particle swarm optimization (PSO) algorithm is utilized to optimize a generalized regression neural network (GRNN) model. Finally, the statistical features are used as input to the neural network for discharge type identification. The results demonstrate that the proposed diagnostic method achieves high accuracy and efficiency in discharge type identification.

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A Comparative Evaluation of Edge Cloud Virtualization Technologies

Cited by 10 publications

References 23 publications

On the Road to 6G: Visions, Requirements, Key Technologies, and Testbeds

On the Road to 6G: Visions, Requirements, Key Technologies, and Testbeds

Modular architecture providing convergent and ubiquitous intelligent connectivity for networks beyond 2030

Partial discharge diagnosis method for electrical equipment based on PSO-GRNN

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