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Dynamics of Research into Modeling the Power Consumption of Virtual Entities Used in the Telco Cloud

This article is a graphical, analytical survey of the literature, over the period 2010–2020, on the measurement of power consumption and relevant power models of virtual entities as they apply to the telco cloud. We present a novel review method, that summarizes the dynamics as well as the results o...

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Detalles Bibliográficos
Autores principales: Depasquale, Etienne-Victor, Davoli, Franco, Rajput, Humaira
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9823787/
https://www.ncbi.nlm.nih.gov/pubmed/36616853
http://dx.doi.org/10.3390/s23010255
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author Depasquale, Etienne-Victor
Davoli, Franco
Rajput, Humaira
author_facet Depasquale, Etienne-Victor
Davoli, Franco
Rajput, Humaira
author_sort Depasquale, Etienne-Victor
collection PubMed
description This article is a graphical, analytical survey of the literature, over the period 2010–2020, on the measurement of power consumption and relevant power models of virtual entities as they apply to the telco cloud. We present a novel review method, that summarizes the dynamics as well as the results of the research. Our method lends insight into trends, research gaps, fallacies and pitfalls. Notably, we identify limitations of the widely used linear models and the progression towards Artificial Intelligence/Machine Learning techniques as a means of dealing with the seven major dimensions of variability: workload type; computer virtualization agents; system architecture and resources; concurrent, co-hosted virtualized entities; approaches towards the attribution of power consumption to virtual entities; frequency; and temperature.
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spelling pubmed-98237872023-01-08 Dynamics of Research into Modeling the Power Consumption of Virtual Entities Used in the Telco Cloud Depasquale, Etienne-Victor Davoli, Franco Rajput, Humaira Sensors (Basel) Review This article is a graphical, analytical survey of the literature, over the period 2010–2020, on the measurement of power consumption and relevant power models of virtual entities as they apply to the telco cloud. We present a novel review method, that summarizes the dynamics as well as the results of the research. Our method lends insight into trends, research gaps, fallacies and pitfalls. Notably, we identify limitations of the widely used linear models and the progression towards Artificial Intelligence/Machine Learning techniques as a means of dealing with the seven major dimensions of variability: workload type; computer virtualization agents; system architecture and resources; concurrent, co-hosted virtualized entities; approaches towards the attribution of power consumption to virtual entities; frequency; and temperature. MDPI 2022-12-26 /pmc/articles/PMC9823787/ /pubmed/36616853 http://dx.doi.org/10.3390/s23010255 Text en © 2022 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
spellingShingle Review
Depasquale, Etienne-Victor
Davoli, Franco
Rajput, Humaira
Dynamics of Research into Modeling the Power Consumption of Virtual Entities Used in the Telco Cloud
title Dynamics of Research into Modeling the Power Consumption of Virtual Entities Used in the Telco Cloud
title_full Dynamics of Research into Modeling the Power Consumption of Virtual Entities Used in the Telco Cloud
title_fullStr Dynamics of Research into Modeling the Power Consumption of Virtual Entities Used in the Telco Cloud
title_full_unstemmed Dynamics of Research into Modeling the Power Consumption of Virtual Entities Used in the Telco Cloud
title_short Dynamics of Research into Modeling the Power Consumption of Virtual Entities Used in the Telco Cloud
title_sort dynamics of research into modeling the power consumption of virtual entities used in the telco cloud
topic Review
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9823787/
https://www.ncbi.nlm.nih.gov/pubmed/36616853
http://dx.doi.org/10.3390/s23010255
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