Power Consumption Estimation Models for Processors, Virtual Machines, and Servers

Möbius, Christoph; Dargie, Waltenegus; Schill, Alexander

doi:10.1109/tpds.2013.183

Cited by 131 publications

(87 citation statements)

References 32 publications

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“…This solution would present the actual real energy consumption and performance of users applications. However, this kind of measurement requires to deploy power models of VMs [23]. Beside the implementation complexity, each power model consumes energy which in total may result in additional energy cost.…”

Section: Discussion On Feasibility and Usabilitymentioning

confidence: 99%

An Experimental Analysis of PaaS Users Parameters on Applications Energy Consumption

Guyon

Orgerie

Morin

2018

2018 IEEE International Conference on Cloud Engineering (IC2E)

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Abstract-Reducing the energy consumed by datacenters becomes of major importance due to the current climate changes and the increasing success of cloud computing. Studies to optimize the energy consumption of cloud systems exist but do not take into account the end-user. The goal of this paper is to understand and quantify the link between the PaaS parameters a user can configure and the application energy consumption. In this work we summarize the parameters available at the PaaS layer and measure their influence on the energy consumed by RUBiS, a web application benchmark. Different database technologies and programming languages are compared as well as their software versions. Experimentation results show that by themselves the existing PaaS parameters can variate the application energy consumption. Although it shows that different programming languages do not consume the same, we discovered that a wider energy difference exists between database technologies which means higher energy savings is possible when the less consuming technology is used. Thus, users could optimize the energy impact of their applications by carefully configuring on-hand PaaS parameters.

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Section: Discussion On Feasibility and Usabilitymentioning

confidence: 99%

An Experimental Analysis of PaaS Users Parameters on Applications Energy Consumption

Guyon

Orgerie

Morin

2018

2018 IEEE International Conference on Cloud Engineering (IC2E)

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“…This can be accomplished by using power models, which correlate the resource usage and the power consumption of individual VMs based on monitored resource usage information [42,75]. Models also allow to estimate the current power consumption of hosts and VMs when direct measurement is not feasible (due to lack of scalability in large clusters, performance overhead of measurements, and cost of devices [59]) and to forecast the power consumption in the future (see Section 3.3).…”

Section: Measurements Vs Modelsmentioning

confidence: 99%

“…linear regression) [59] but also more novel methods such as artificial neural networks [27]. Those are very powerful techniques to capture data correlations, but they might require driving the mining process to achieve better accuracy and reduce the computational complexity.…”

Section: Measurements Vs Modelsmentioning

confidence: 99%

Toward sustainable data centers: a comprehensive energy management strategy

Guitart

2016

Computing

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Data centers are major contributors to the emission of carbon dioxide to the atmosphere, and this contribution is expected to increase in the following years. This has encouraged the development of techniques to reduce the energy consumption and the environmental footprint of data centers. Whereas some of these techniques have succeeded to reduce the energy consumption of the hardware equipment of data centers (including IT, cooling, and power supply systems), we claim that sustainable data centers will be only possible if the problem is faced by means of a holistic approach that includes not only the aforementioned techniques but also intelligent and unifying solutions that enable a synergistic and energy-aware management of data centers. In this paper, we propose a comprehensive strategy to reduce the carbon footprint of data centers that uses the energy as a driver of their management procedures. In addition, we present a holistic management architecture for sustainable data centers that implements the aforementioned strategy, and we propose design guidelines to accomplish each step of the proposed strategy, referring to related achievements and enumerating the main challenges that must be still solved.

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“…The disadvantage is that the model is complex and the model accuracy is low. Christoph Mobius et al [3] provides a power consumption model based on CPU utilization ratio, and they provide three kinds of model which is the utilization ratio of CPU model, VM model and server model. The relative error of the model is between 0-15%, and the error is caused by the choice of Benchmark program in the modeling process.…”

Section: Introductionmentioning

confidence: 99%

Research on Energy Consumption Modeling Method for Multi-Type Hybrid Applications

Guo¹,

Wang²

2017

Proceedings of the 2017 International Conference on Applied Mathematics, Modelling and Statistics Application (AMMSA 2017)

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Abstract-With the rapid development of cloud computing, a large number of datacenters were built. These datacenters provide excellent computing resource and service to people. However, they also bring huge energy consumption at the same time. Scientists all around the world try to solve this problem, for which the first step is to build an accurate power consumption model. Some related work aims to establish a model which only can be applied to a single-type application. Nevertheless, multi-type applications are often executed simultaneously in practice, and it is difficult to establish a multi-type application power consumption model. In this paper, we design a multi-type application power consumption model based on the hardware resource utilization ratio, recording the server power at different hardware resource utilization ratio. Using hardware resource utilization ratio as input and the power as output, we build the model by leveraging the BP neural network method. Finally, the accuracy of our method can reach 95%.

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Power Consumption Estimation Models for Processors, Virtual Machines, and Servers

Cited by 131 publications

References 32 publications

An Experimental Analysis of PaaS Users Parameters on Applications Energy Consumption

An Experimental Analysis of PaaS Users Parameters on Applications Energy Consumption

Toward sustainable data centers: a comprehensive energy management strategy

Research on Energy Consumption Modeling Method for Multi-Type Hybrid Applications

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