A new approach to model energy consumption of servers in data centers

Warkozek, Ghaith; Drayer, Elisabeth; Debusschere, Vincent; Bacha, Seddik

doi:10.1109/icit.2012.6209940

Cited by 37 publications

(30 citation statements)

References 7 publications

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“…Some other models can be found where server power is formulated as a quadratic function of the CPU usage [17,18,19]. Still, as opposed to ours, these models do not include the estimation of the static power consumption (which has turned to have a great impact due to the current server technology).…”

Section: Related Workmentioning

confidence: 96%

Server Power Modeling for Run-time Energy Optimization of Cloud Computing Facilities

et al. 2014

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As advanced Cloud services are becoming mainstream, the contribution of data centers in the overall power consumption of modern cities is growing dramatically. The average consumption of a single data center is equivalent to the energy consumption of 25.000 households. Modeling the power consumption for these infrastructures is crucial to anticipate the effects of aggressive optimization policies, but accurate and fast power modeling is a complex challenge for high-end servers not yet satisfied by analytical approaches. This work proposes an automatic method, based on Multi-Objective Particle Swarm Optimization, for the identification of power models of enterprise servers in Cloud data centers. Our approach, as opposed to previous procedures, does not only consider the workload consolidation for deriving the power model, but also incorporates other non traditional factors like the static power consumption and its dependence with temperature. Our experimental results shows that we reach slightly better models than classical approaches, but simultaneously simplifying the power model structure and thus the numbers of sensors needed, which is very promising for a short-term energy prediction. This work, validated with real Cloud applications, broadens the possibilities to derive efficient energy saving techniques for Cloud facilities.

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Section: Related Workmentioning

confidence: 96%

Server Power Modeling for Run-time Energy Optimization of Cloud Computing Facilities

et al. 2014

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“…In order to determine the power consumption of an idle hypervisor, Warkozek et al [19] showed that P hyper i,j is proportional to the number of Avatars hosted in the server, i.e., the more Avatars server j hosts, the more power is consumed on the hypervisor for configuring these Avatars:…”

Section: A Server Power Consumption Modelmentioning

confidence: 99%

“…As mentioned earlier, P vir i,j is the power consumption of server j for performing virtualization, and it includes two parts [19]:…”

Section: A Server Power Consumption Modelmentioning

confidence: 99%

Green Energy Aware Avatar Migration Strategy in Green Cloudlet Networks

Sun

Ansari

Fan

2015

2015 IEEE 7th International Conference on Cloud Computing Technology and Science (CloudCom)

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“…The authors develop linear regression models that present the power consumption of a server as a linear function of its CPU usage [22], [28]. Some other models can be found where server power is formulated as a quadratic function of the CPU usage [24], [34]. Still, as opposed to ours, these models do not include the estimation of the static power consumption (which has turned to have a great impact due to the current server technology).…”

Section: Related Workmentioning

confidence: 99%

Enhancing Regression Models for Complex Systems Using Evolutionary Techniques for Feature Engineering

et al. 2014

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This work proposes an automatic methodology for modeling complex systems. Our methodology is based on the combination of Grammatical Evolution and classical regression to obtain an optimal set of features that take part of a linear and convex model. This technique provides both Feature Engineering and Symbolic Regression in order to infer accurate models with no effort or designer's expertise requirements. As advanced Cloud services are becoming mainstream, the contribution of data centers in the overall power consumption of modern cities is growing dramatically. These facilities consume from 10 to 100 times more power per square foot than typical office buildings. Modeling the power consumption for these infrastructures is crucial to anticipate the effects of aggressive optimization policies, but accurate and fast power modeling is a complex challenge for high-end servers not yet satisfied by analytical approaches. For this case study, our methodology minimizes error in power prediction. This work has been tested using real Cloud applications resulting on an average error in power estimation of 3.98%. Our work improves the possibilities of deriving Cloud energy efficient policies in Cloud data centers

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A new approach to model energy consumption of servers in data centers

Cited by 37 publications

References 7 publications

Server Power Modeling for Run-time Energy Optimization of Cloud Computing Facilities

Server Power Modeling for Run-time Energy Optimization of Cloud Computing Facilities

Green Energy Aware Avatar Migration Strategy in Green Cloudlet Networks

Enhancing Regression Models for Complex Systems Using Evolutionary Techniques for Feature Engineering

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