E-mc2: A formal framework for energy modelling in cloud computing

Castañé, Gabriel G.; Núñez, Alberto; Llopis, Pablo; Carretero, Jesús

doi:10.1016/j.simpat.2013.05.002

Cited by 54 publications

(19 citation statements)

References 31 publications

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“…The search for more environmental friendly information technology (IT) has already had an impact on the energy awareness and energy consumption of the sector [3]. A distinction is often made between 'greening by IT', i.e., using IT to make other industries more sustainable, and 'greening of IT', the process of making IT itself more sustainable.…”

Section: Introductionmentioning

confidence: 99%

Profiling energy profilers

Jagroep

Werf

Jansen

et al. 2015

Proceedings of the 30th Annual ACM Symposium on Applied Computing

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While energy is directly consumed by hardware, it is the software that provides the instructions to do so. Energy profilers provide a means to measure the energy consumption of software, enabling the user to take measures in making software more sustainable. Although each energy profiler has access to roughly the same data, the reported measurements can differ significantly between energy profilers. In this research, energy profilers are evaluated through a series of experiments on their functionality and the accuracy of the reported measurements. The results show that there is still work to be done before these software tools can be safely used for their intended purpose. As a start, a correction factor is suggested for the energy profilers.

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Section: Introductionmentioning

confidence: 99%

Profiling energy profilers

Jagroep

Werf

Jansen

et al. 2015

Proceedings of the 30th Annual ACM Symposium on Applied Computing

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“…Direct measurement obtains the wall power [13] value via the use of Watt meters [9], providing an aggregation of the current power usage of a physical resource [4]. Performance counters [3,10,22] are a non-invasive means of determining energy usage, by utilising performance counters located within the CPU and Operating System.…”

Section: Related Workmentioning

confidence: 99%

Towards an Energy-Aware Cloud Architecture for Smart Grids

Kavanagh

Armstrong

Djemame

et al. 2016

Economics of Grids, Clouds, Systems, and Services

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Abstract. Energy consumption in Cloud computing is a significant issue in regards to aspects such as the cost of energy, cooling in the data center and the environmental impact of cloud data centers. Smart grids offers the prospect of dynamic costs for a data center's energy usage. These dynamic costs can be passed on to Cloud users providing incentives for users to moderate their load while also ensuring the Cloud providers are insulated from fluctuations in the cost of energy. The first step towards this is an architecture that focuses on energy monitoring and usage prediction. We provide such an architecture at both the PaaS and IaaS layers, resulting in energy metrics for applications, VMs and physical hosts, which is key to enabling active demand in cloud data centers. This architecture is demonstrated through our initial results utilising a generic use case, providing energy consumption information at the PaaS and IaaS layers. Such monitoring and prediction provides the groundwork for providers passing on energy consumption costs to end users. It is envisaged that the resulting varying price associated with energy consumption can help motivate the formation of methods and tools to support software developers aiming to optimise energy efficiency and minimise the carbon footprint of Cloud applications.

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“…Direct measurement obtains the wall power [6] value via the use of Watt meters [5], providing an aggregation of the current power usage of a physical resource [7]. Performance counters [8]- [10] are a non-invasive means of determining energy usage, by utilising performance counters located within the CPU and Operating System.…”

Section: Related Workmentioning

confidence: 99%

Accuracy of Energy Model Calibration with IPMI

Kavanagh

Armstrong

Djemame

2016

2016 IEEE 9th International Conference on Cloud Computing (CLOUD)

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Abstract-Energy consumption in Cloud computing is a significant issue and affects aspects such as the cost of energy, cooling in the data center and the environmental impact of cloud data centers. Monitoring and prediction provides the groundwork for improving the energy efficiency of data centers. This monitoring however is required to be fast and efficient without unnecessary overhead. It is also required to scale to the size of a data center where measurement through directly attached Watt meters is unrealistic. This therefore requires models that translate resource utilisation into the power consumed by a physical host. These models require calibrating and are hence subject to error. We discuss the causes of error within these models, focusing upon the use of IPMI in order to gather this data. We make recommendations on ways to mitigate this error without overly complicating the underlying model. The final result of these models is a Watt meter emulator that can provide values for power consumption from hosts in the data center, with an average error of 0.20W.

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E-mc2: A formal framework for energy modelling in cloud computing

Cited by 54 publications

References 31 publications

Profiling energy profilers

Profiling energy profilers

Towards an Energy-Aware Cloud Architecture for Smart Grids

Accuracy of Energy Model Calibration with IPMI

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