Virtual machine power metering and provisioning

Kansal, Aman; Zhao, Feng; Liu, Jie; Kothari, Nupur; Bhattacharya, Arka

doi:10.1145/1807128.1807136

Cited by 460 publications

(334 citation statements)

References 25 publications

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“…These models do not relate the power consumption of the system with the applications. Recent estimation-based power models tried to estimate the power consumption of application running on desktops (Zeng et al, 2002), sensor nodes (Shnayder et al, 2004), virtual machines (Kansal et al, 2010) and more recently, mobile phones (Zhang et al, 2010).…”

Section: Estimation-based Methodsmentioning

confidence: 99%

Measuring Power Consumption for Image Processing on Android Smartphone

Saipullah

Anuar

Ismail

et al. 2012

American Journal of Applied Sciences

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The energy consumption of smartphones can be undertaken in multiple levels of hardware and software. Generally, there are two approaches in measuring power consumption of a smartphone application which are the measurement-based and estimation-based methods. The goal of this study is to compare the two power consumption measuring approaches in quantifying the power consumed by image processing applications in Android smartphone. For measurement-based approach, a simple wattmeter is designed whereas for the estimation-based approach, an Android application called the PowerTutor will be utilized. The wattmeter and PowerTutor will measure the power consumption of eight image processing methods running on modified Android library with self-implemented algorithm called the CamTest. According to t-test analysis that has been conducted, the p values of all of the image processing methods show that there are no significant differences between the wattmeter and the PowerTutor application (p>0.01). Even though measurement-based method is more accurate than estimation-based method in term of measuring power consumption, PowerTutor application proved it provides accurate, real-time power consumption estimation for Android platform smartphones. Application developers still can use PowerTutor as an option to determine the impact of software design on power consumption.

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Section: Estimation-based Methodsmentioning

confidence: 99%

Measuring Power Consumption for Image Processing on Android Smartphone

Saipullah

Anuar

Ismail

et al. 2012

American Journal of Applied Sciences

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“…In order to do so, power usage models are normally used where performance characteristics are being used for the modeling of the energy consumption. In [10] [11] [12] [13] it is pointed out that CPU utilization is the main factor driving energy consumption of a computing system, with memory and disk resource utilization playing a secondary role.…”

Section: State Of the Art And Related Workmentioning

confidence: 99%

Estimation and forecasting of ecological efficiency of virtual machines

Katsaros

Stichler

Subirats

et al. 2016

Future Generation Computer Systems

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The massive development of the cloud marketplace is leading to an increase in the number of the Data Centers (DCs) globally and eventually to an increase of the CO 2 related footprint. The calculation of the impact of Virtual Machines (VMs) on the environment is a challenging task, not only due to the technical difficulties but also due to the lack of information from the energy providers. The ecological efficiency of a system captures the relationship between the performance of the system with its environmental footprint. In this paper we present a methodology for the estimation and prediction of the ecological efficiency of VMs in private cloud infrastructures. We specifically focus on the information management starting from the energy resources in a region, the energy consumption and the performance of the resources and finally the calculation of ecological efficiency of a VM. To this end, we have designed and implemented a framework through which the ecological efficiency of a running VM can be assessed and the ecological efficiency of a VM to be deployed can be forecasted. The presented framework is being evaluated through several private cloud scenarios with VM deployments in hosts located in Germany.

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“…Joulemeter [Kansal et al 2010] is a system to attribute the energy consumption of a cloud server to the running virtual machines (VMs). The energy consumption of each VM is estimated based on measurementbased energy models.…”

Section: Energy Accountingmentioning

confidence: 99%

Fairness and Incentive Considerations in Energy Apportionment Policies

Vergara

Nadjm‐Tehrani

Asplund

2016

ACM Trans. Model. Perform. Eval. Comput. Syst.

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The energy consumption of a system is determined by the system component usage patterns and interactions between the coexisting entities and resources. Energy accounting plays an essential role to reveal the contribution of each entity to the total consumption and for energy management. Unfortunately, energy accounting inherits the apportionment problem of accounting in general, which does not have a general single best solution. In this paper we leverage cooperative game theory commonly used in cost allocation problems to study the energy apportionment problem, i.e., the problem of prescribing the actual energy consumption of a system to the consuming entities (e.g., applications, processes or users of the system).We identify five relevant fairness properties for energy apportionment and present a detailed categorisation and analysis of eight previously proposed energy apportionment policies from different fields in computer and communication systems. In addition, we propose two novel energy apportionment policies based on cooperative game theory which provide strong fairness notion and a rich incentive structure. Our comparative analysis in terms of the identified five fairness properties as well as information requirement and computational complexity shows that there is a trade-off between fairness and the other evaluation criteria. We provide guidelines to select an energy apportionment policy depending on the purpose of the apportionment and the characteristics of the system.

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Virtual machine power metering and provisioning

Cited by 460 publications

References 25 publications

Measuring Power Consumption for Image Processing on Android Smartphone

Measuring Power Consumption for Image Processing on Android Smartphone

Estimation and forecasting of ecological efficiency of virtual machines

Fairness and Incentive Considerations in Energy Apportionment Policies

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