iMeter: An integrated VM power model based on performance profiling

Yang, Hailong; Zhao, Qi; Luan, Zhongzhi; Qian, Depei

doi:10.1016/j.future.2013.07.008

Cited by 34 publications

(40 citation statements)

References 32 publications

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“…They are usually based on counters (hardware or software) in order to monitor the resource usage. Their accuracy thus depends which resources are selected, how they are monitored and which formulas are used to estimate the VM power consumption from the monitoring data, such as linear regression (Kim et al, 2011) (Wu et al, 2016), polynomial regression (Xiao et al, 2013), machine learning (Yang et al, 2014) or tree regression based approach (Gu et al, 2015). In these studies, estimation errors typically fluctuate from 2 to 5%.…”

Section: Related Workmentioning

confidence: 99%

A CO2 Emissions Accounting Framework with Market-based Incentives for Cloud Infrastructures

Margery

Guyon²,

Orgerie

et al. 2017

Proceedings of the 6th International Conference on Smart Cities and Green ICT Systems

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Abstract:CO2 emissions related to Cloud computing reach nowadays worrying levels, without any reduction in sight. Often, Cloud users, asking for virtual machines, are not aware of such emissions which concern the entire Cloud infrastructures and are thus difficult to split into the actual resources utilization, such as virtual machines. We propose a CO2 emissions accounting framework giving flexibility to the Cloud providers, predictability to the users and allocating all the carbon costs to the users. This paper shows the architecture of our accounting framework and ideas on how to practically implement it.

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Section: Related Workmentioning

confidence: 99%

A CO2 Emissions Accounting Framework with Market-based Incentives for Cloud Infrastructures

Margery

Guyon²,

Orgerie

et al. 2017

Proceedings of the 6th International Conference on Smart Cities and Green ICT Systems

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“…However, they cannot account for the energy consumption of individual virtual machines (VMs) or services running on each host, which is required for instance to implement effective power-aware scheduling policies. 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%

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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“…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. Wall power measurements have the advantage of accuracy but require the specialist physical hardware to be attached into the infrastructure, while the performance counters are indirect measures of power consumption and requires a model to derive an estimate of the energy consumed.…”

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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iMeter: An integrated VM power model based on performance profiling

Cited by 34 publications

References 32 publications

A CO2 Emissions Accounting Framework with Market-based Incentives for Cloud Infrastructures

A CO2 Emissions Accounting Framework with Market-based Incentives for Cloud Infrastructures

Toward sustainable data centers: a comprehensive energy management strategy

Towards an Energy-Aware Cloud Architecture for Smart Grids

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