Virtual machine power measuring technique with bounded error in cloud environments

Hu, Zhigang; Liu, Dongbo; Yan, Guofeng; Qu, Xiaobo

doi:10.1016/j.jnca.2012.12.002

Cited by 38 publications

(17 citation statements)

References 24 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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“…To allow feedback on the power consumption, there is a need for a model that maps the physical power consumption to the virtual machines. Such models are provided in [22,23] and show sufficient accuracy to be employed in our proposed architecture. Measuring the individual power consumption of VMs executed on a physical machine results in a higher power consumption per VM if the machine is under utilized and the static idle power is shared [24,25].…”

Section: Related Workmentioning

confidence: 99%

“…The rest of this subsection is focusing on the behavior of this component and how it estimates the energy consumption of a particular VM. There has been significant research on the runtime behavior of VMs and their energy characteristics [23,25]. So, these energy models can be utilized to determine E R (VM ).…”

Section: Accountingmentioning

confidence: 99%

“…In the followings we discuss how these components are considered in our accounting database. Albeit, related works (e.g., [23,25]) vary on how they map the idle consumption of the host to a particular VM, they all agree that this component is heavily dependent on the number and kind of virtual machines that share the physical host. Therefore, the virtual machine placement heavily impacts how the idle component of the VM is evaluated: by opting for multi-tenancy or by choosing machines with smaller idle consumption academics could reduce the idle component of their VMs.…”

Section: Accountingmentioning

confidence: 99%

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An architecture to stimulate behavioral development of academic cloud users

Kecskeméti

Ostermann

Prodan

2014

Sustainable Computing: Informatics and Systems

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Academic cloud infrastructures are constructed and maintained so they minimally constrain their users. Since they are free and do not limit usage patterns, academics developed such behavior that jeopardizes fair and flexible resource provisioning. For efficiency, related work either explicitly limits user access to resources, or introduce automatic rationing techniques. Surprisingly, the root cause (i.e., the user behavior) is disregarded by these approaches. This article compares academic cloud user behavior to its commercial equivalent. We deduce, that academics should behave like commercial cloud users to relieve resource provisioning. To encourage commercial like behavior, we propose an architectural extension to existing academic infrastructure clouds. First, every user's energy consumption and efficiency is monitored. Then, energy efficiency based leader boards are used to ignite competition between academics and reveal their worst practices. Leader boards are not sufficient to completely change user behavior. Thus, we introduce engaging options that encourage academics to delay resource requests and prefer resources more suitable for the infrastructure's internal provisioning. Finally, we evaluate our extensions via a simulation using real life academic resource request traces. We show a potential resource utilization reduction (by the factor of at most 2.6) while maintaining the unlimited nature of academic clouds.

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“…Although this technology is proved to be effective there are some challenging issues such as the capability of the server is multiplexed hence measuring per VM power will be complicated. (Peng Xiao et al, 2012). In order to share the applications in the cloud computing atmosphere the performance that is QoS (Quality of service) should be considered.…”

Section: Introductionmentioning

confidence: 99%

A Vm Scheduling Algorithm for Reducing Power Consumption of a Virtual Machine in Cloud Environments

Nithya¹

2014

IJRET

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Virtual machine power measuring technique with bounded error in cloud environments

Cited by 38 publications

References 24 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

An architecture to stimulate behavioral development of academic cloud users

A Vm Scheduling Algorithm for Reducing Power Consumption of a Virtual Machine in Cloud Environments

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