A combined frequency scaling and application elasticity approach for energy-efficient cloud computing

Tesfatsion, Selome Kostentinos; Wadbro, Eddie; Tordsson, Johan

doi:10.1016/j.suscom.2014.08.007

Cited by 23 publications

(13 citation statements)

References 4 publications

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“…For that, they will use the power models described in the previous section to forecast the energy consumption of VMs and physical hosts according to their predicted resource usage and the status of the infrastructure. In addition, DVFS and elasticity of VMs can be used to adapt the computing capacity of the hosts and the VMs to the intensity of the workloads they have to execute [65].…”

Section: Energy-driven Managementmentioning

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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Section: Energy-driven Managementmentioning

confidence: 99%

Toward sustainable data centers: a comprehensive energy management strategy

Guitart

2016

Computing

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“…More techniques and methodologies have been proposed based on virtualization [82] or assigning the resources according to queuing model [55] to reduce CO 2 emission and cutting down the power consumption. Combine some power management techniques such as changing the number of VMs or cores, and DVFS enabled Cloud providers to minimize power consumption as well as addressing the high operating costs and carbon footprint [77]. Green IT framework in view of power efficiency and reducing the effects of global warming can be achieved by using energy-efficient techniques like virtualization and can be effectively applied for large and complex server farms according to green metrics such as Power Usage Effectiveness (PUE), Data center Effectiveness and carbon emission [80].…”

Section: Related Workmentioning

confidence: 99%

Evaluating power efficient algorithms for efficiency and carbon emissions in cloud data centers: A review

Uddin

Darabidarabkhani

Shah

et al. 2015

Renewable and Sustainable Energy Reviews

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“…Optimal configuration is taken care to minimize the energy consumption and satisfies various performance objectives [2]. A flexible load balancing traffic grooming strategy is maintained for system optimization.…”

Section: Related Workmentioning

confidence: 99%

Evaluation of Auto Scaling and Load Balancing Features in Cloud

Ashalatha¹,

Agarkhed²

2015

IJCA

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Cloud computing is a latest technology that uses internet and centralized servers to maintain data and various types of applications. Cloud computing allows consumers and business people to use applications without any installation of either hardware or software and accessing their personal files at any computer with internet access. This technology allows for much more efficient computing by centralizing storage, memory, processing. The cloud computing system is the newer version of utility computing which has replaced its area at various data centers. The Load balancer determines when to start or end any virtual machine in the Cloud. The auto scaling feature along with the load balancing technique makes anyone easy to automatically increase or decrease back-end capacity to meet traffic fluctuation levels.

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A combined frequency scaling and application elasticity approach for energy-efficient cloud computing

Cited by 23 publications

References 4 publications

Toward sustainable data centers: a comprehensive energy management strategy

Toward sustainable data centers: a comprehensive energy management strategy

Evaluating power efficient algorithms for efficiency and carbon emissions in cloud data centers: A review

Evaluation of Auto Scaling and Load Balancing Features in Cloud

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