research-article

Energy-efficient cloud data center with fair service level agreement for green computing

Author:

Ming-Jeng YangAuthors Info & Claims

Cluster Computing, Volume 24, Issue 4

Pages 3337 - 3349

https://doi.org/10.1007/s10586-021-03342-z

Published: 01 December 2021 Publication History

Abstract

More and more cloud data centers provide numerous cloud computing services. However, how to meet customer needs, improve efficiency and reduce costs are important issues that cloud service providers must deal with. For customers, it is very important to consider the quality of service requirements provided by the data center providing public cloud services. Besides, data center operators should consider how to reduce energy consumption. Therefore, for these important issues, we propose a possible balance between service quality and energy conservation strategy. We find the relationship between the minimal service resources and the required level of services. Under conditions consistent with the SLA, our strategy quantifies the quality of service and calculates the required computing resources according to changes in workload to achieve an energy-saving goal. Also, the policy approximate function is derived and can achieve efficient decision-made goals.

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Cited By

Meera SValarmathi K(2024)Optimizing cloud resource allocationJournal of Intelligent & Fuzzy Systems: Applications in Engineering and Technology10.3233/JIFS-23405446:1(2311-2330)Online publication date: 1-Jan-2024
https://dl.acm.org/doi/10.3233/JIFS-234054
Zhou PLin LZhang ZDeng YHe T(2024)GHB: a cost-effective and energy-efficient data center network structure with greater incremental scalabilityCluster Computing10.1007/s10586-022-03849-z27:1(91-107)Online publication date: 1-Feb-2024
https://dl.acm.org/doi/10.1007/s10586-022-03849-z
Aghasi AJamshidi KBohlooli A(2022)A thermal-aware energy-efficient virtual machine placement algorithm based on fuzzy controlled binary gravitational search algorithm (FC-BGSA)Cluster Computing10.1007/s10586-021-03476-025:2(1015-1033)Online publication date: 1-Apr-2022
https://dl.acm.org/doi/10.1007/s10586-021-03476-0

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Published In

cover image Cluster Computing

Cluster Computing Volume 24, Issue 4

Dec 2021

1105 pages

ISSN:1386-7857

Issue’s Table of Contents

© The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2021.

Publisher

Kluwer Academic Publishers

United States

Publication History

Published: 01 December 2021

Accepted: 10 June 2021

Revision received: 09 June 2021

Received: 10 April 2020

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Citations

Cited By

Meera SValarmathi K(2024)Optimizing cloud resource allocationJournal of Intelligent & Fuzzy Systems: Applications in Engineering and Technology10.3233/JIFS-23405446:1(2311-2330)Online publication date: 1-Jan-2024
https://dl.acm.org/doi/10.3233/JIFS-234054
Zhou PLin LZhang ZDeng YHe T(2024)GHB: a cost-effective and energy-efficient data center network structure with greater incremental scalabilityCluster Computing10.1007/s10586-022-03849-z27:1(91-107)Online publication date: 1-Feb-2024
https://dl.acm.org/doi/10.1007/s10586-022-03849-z
Aghasi AJamshidi KBohlooli A(2022)A thermal-aware energy-efficient virtual machine placement algorithm based on fuzzy controlled binary gravitational search algorithm (FC-BGSA)Cluster Computing10.1007/s10586-021-03476-025:2(1015-1033)Online publication date: 1-Apr-2022
https://dl.acm.org/doi/10.1007/s10586-021-03476-0

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