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International Conference on Business Information Systems

BIS 2019: Business Information Systems pp 514–525Cite as

Energy Efficient Cloud Data Center Using Dynamic Virtual Machine Consolidation Algorithm

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Part of the book series: Lecture Notes in Business Information Processing ((LNBIP,volume 353))

Abstract

In Cloud Data centers, virtual machine consolidation on minimizing energy consumed aims at reducing the number of active physical servers. Dynamic consolidation of virtual machines (VMs) and switching idle nodes off allow Cloud providers to optimize resource usage and reduce energy consumption. One aspect of dynamic VM consolidation that directly influences Quality of Service (QoS) delivered by the system is to determine the best moment to reallocate VMs from an overloaded or undeloaded host. In this article we focus on energy-efficiency of Cloud datacenter using Dynamic Virtual Machine Consolidation Algorithms by planetLab workload traces, which consists of a thousand PlanetLab VMs with large-scale simulation environments. Experiments are done in a simulated cloud environment by the CloudSim simulation tool. The obtained results show that consolidation reduces the number of migrations and the power consumption of the servers. Also application performances are improved.

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Author information

Authors and Affiliations

  1. Université de Thiès, Thies, Senegal

    Cheikhou Thiam

  2. Université Gaston Berger, Saint-Louis, Senegal

    Fatoumata Thiam

Authors
  1. Cheikhou Thiam
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  2. Fatoumata Thiam
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Corresponding author

Correspondence to Fatoumata Thiam .

Editor information

Editors and Affiliations

  1. Poznań University of Economics and Business, Poznań, Poland

    Witold Abramowicz

  2. ETSI Informática, University of Seville, Seville, Spain

    Rafael Corchuelo

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Thiam, C., Thiam, F. (2019). Energy Efficient Cloud Data Center Using Dynamic Virtual Machine Consolidation Algorithm. In: Abramowicz, W., Corchuelo, R. (eds) Business Information Systems. BIS 2019. Lecture Notes in Business Information Processing, vol 353. Springer, Cham. https://doi.org/10.1007/978-3-030-20485-3_40

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  • DOI: https://doi.org/10.1007/978-3-030-20485-3_40

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-20484-6

  • Online ISBN: 978-3-030-20485-3

  • eBook Packages: Computer ScienceComputer Science (R0)

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