System Power Model and Virtual Machine Power Metering for Cloud Computing Pricing

Chen, Wen; Long, Xiang; Yang, Yang; Ni, Fan; Mu, Yifen

doi:10.1109/isdea.2012.327

Cited by 9 publications

(24 citation statements)

References 6 publications

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“…Estimation of power consumption of (virtualization-) host system [70][71][72][73][74][75][76][77][78][79][80][81][82]. Measurement of a single server's power consumption through the use of an external power meter is a trivial task.…”

Section: P3mentioning

confidence: 99%

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A Categorization of Power Measurement and Power Consumption Models in the Telco Cloud

Depasquale

Salam

Davoli

2021

Preprint

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This article surveys the literature, over the period 2010-2020, on measurement of power consumption and relevant power models of virtual entities as they apply to the telco cloud. Hardware power meters are incapable of measuring power consumption of individual virtual entities co-hosted on a physical machine. Thus, software power meters are inevitable, yet their development is difficult. Indeed, there is no direct approach to measurement and, therefore, modeling through proxies of power consumption must be used. In this survey, we present trends, 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 attribution of power consumption to virtual entities; frequency; and temperature.

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Section: P3mentioning

confidence: 99%

“…1 Adaptation to change in the number of co-hosted, concurrent VEs: is widely achieved through time-division multiplexing of event counters [72,75,82,91,98],…”

Section: A17: Model Adaptation Techniquementioning

confidence: 99%

A Categorization of Power Measurement and Power Consumption Models in the Telco Cloud

Depasquale

Salam

Davoli

2021

Preprint

View full text Add to dashboard Cite

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“…The implementation of JVM is operating system dependent and significantly influences the measurements. Except the above mentioned tools have been published many methodologies [14] [15], strategies [16] [17] or reviews for better virtualization overhead measurement understanding [18] [19].…”

Section: Related Work Overviewmentioning

confidence: 99%

CloudEVBench - Virtualization Technology Efficiency Testing Tool for the Distributed Infrastructures

Fesl¹,

Cehák²,

Doležalová³

et al. 2016

IJGDC

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The virtualized systems are today a very popular topic and their using plays the great role in current datacenters. The Virtual Technology Efficiency TestingThe main aspect in using and deployment of the virtualization technology is its efficiency. The substance of this technology is as follows. The resources of one powerful physical machine can be shared between many simultaneously executed virtual computers. The efficiency of the virtualization technology is an important factor, which determines the possible count of the simultaneously executable machines.Another aspect, which also fundamentally affects the efficiency, is the type of used virtualization technique. The main virtualization types are the full virtualization, hardware assisted virtualization, paravirtualization, partial virtualization and emulation [3]. Today is predominantly used the full virtualization technology. The term "virtualization" will always mean the hardware assisted virtualization technology in the following text.

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“…Essa dependência de hardware externo torna a proposta inviável em larga escala. [Wen et al 2013] apresenta uma solução queé específica para o hipervisor L4 [Fiasco 2015]. Os consumos de energia das VMs são estimados por um modelo energético que infere os dados a partir de performance counters.…”

Section: Trabalhos Relacionadosunclassified

Um Monitor de Consumo Energético para Computação em Nuvem na Ferramenta OpenNebula

Leiria¹,

Schepke²,

Mello³

et al. 2016

Anais Do XVII Simpósio Em Sistemas Computacionais De Alto Desempenho (SSCAD 2016)

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Nuvens computacionais consomem grandes quantidades de energia elétrica, sendo responsáveis pela emissão de pelo menos 2% do dióxido de carbono mundial. Por outro lado, as ferramentas de gerenciamento de nuvens atuais não dispõem de recursos para monitorar o consumo energético de suas infraestruturas, bem como não oferecem informações sobre a demanda de energia elétrica, sendo este uma parte integrante do custo de manutenção da nuvem. Portanto, este artigo propõe um modelo de monitoramento energético para nuvens computacionais. Foi criado um addon denominado Monitor Energético (ME) para monitorar o consumo energético em data centers virtualizados, através de Kernel-based Virtual Machine e geridos por OpenNebula. Os experimentos foram realizados com a ferramenta Sysbench para o estresse proposital do ambiente de testes, aonde os resultados comprovaram o bom funcionamento da nossa ferramenta e que ela possui uma visualização intuitiva do monitoramento.

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System Power Model and Virtual Machine Power Metering for Cloud Computing Pricing

Cited by 9 publications

References 6 publications

A Categorization of Power Measurement and Power Consumption Models in the Telco Cloud

A Categorization of Power Measurement and Power Consumption Models in the Telco Cloud

CloudEVBench - Virtualization Technology Efficiency Testing Tool for the Distributed Infrastructures

Um Monitor de Consumo Energético para Computação em Nuvem na Ferramenta OpenNebula

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