Modelling Energy Efficient Server Management Policies in PEPA

Alssaiari, Ali; Gining, Ray Adderley Jm; Thomas, Nigel

doi:10.1145/3053600.3053609

Cited by 2 publications

(2 citation statements)

References 14 publications

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“…Some research approaches are energy-oriented workload dispatch [7]- [9], [34] and server consolidation [10]- [17], [33] which dynamically adjust the active server set in order to turn off a portion of servers and save computing energy without compromising the quality of service.…”

Section: Computing Energy Saving Scheduling Methodsmentioning

confidence: 99%

Trading-Off Computing and Cooling Energies by VM Migration in Data Centers

Song

Zhao

Wang

et al. 2018

IEICE Trans. Inf. & Syst.

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High energy cost is a big challenge faced by the current data centers, wherein computing energy and cooling energy are main contributors to such cost. Consolidating workload onto fewer servers decreases the computing energy. However, it may result in thermal hotspots which typically consume greater cooling energy. Thus the tradeoff between computing energy decreasing and cooling energy decreasing is necessary for energy saving. In this paper, we propose a minimized-total-energy virtual machine (VM for short) migration model called C 2 vmMap based on efficient tradeoff between computing and cooling energies, with respect to two relationships: one for between the resource utilization and computing power and the other for among the resource utilization, the inlet and outlet temperatures of servers, and the cooling power. Regarding online resolution of the above model for better scalability, we propose a VM migration algorithm called C 2 vmMap heur to decrease the total energy of a data center at run-time. We evaluate C 2 vmMap heur under various workload scenarios. The real server experimental results show that C 2 vmMap heur reduces up to 40.43% energy compared with the non-migration load balance algorithm. This algorithm saves up to 3x energy compared with the existing VM migration algorithm.

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Section: Computing Energy Saving Scheduling Methodsmentioning

confidence: 99%

Trading-Off Computing and Cooling Energies by VM Migration in Data Centers

Song

Zhao

Wang

et al. 2018

IEICE Trans. Inf. & Syst.

View full text Add to dashboard Cite

show abstract

“…The third variable that supposedly influences the environmental context in GITL is environmental sustainability motivating forces, as explained by the theory of planned behavior (TPB) [32], which refers to the social [91], ecological [92], [93], and end-company prestige implications [94] that organizations adopt to improve their image and favor planet care while optimizing resources and productivity.…”

Section: Environmental Sustainability Motivating Forces (Esm)mentioning

confidence: 99%

Model for the Intent to Adopt Green IT in the Context of Organizations

et al. 2022

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In organizations, part of sustainability is manifested through green information technology (GIT) for the use of information technologies (IT) resources in a profitable and sustainable manner. The purpose of this study was to understand the social, individual, and contextual factors that drive actors belonging to the sector of organizations in the Latin American and Caribbean (LAC) context on the intention to adopt GIT to generate strategies that promote its application. A model resulting from the analysis and integration of adoption models was proposed, including the theory of planned behavior (TPB), norm activation theory (NAT), GIT adoption model (GITAM), and environmental sustainability variables. To determine the predictive factors, the research team collected data from organizations belonging to Mexico,

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Modelling Energy Efficient Server Management Policies in PEPA

Cited by 2 publications

References 14 publications

Trading-Off Computing and Cooling Energies by VM Migration in Data Centers

Trading-Off Computing and Cooling Energies by VM Migration in Data Centers

Model for the Intent to Adopt Green IT in the Context of Organizations

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