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Many-Objective Optimization for Virtual Machine Placement in Cloud Computing

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Research Advances in Cloud Computing

Abstract

Resource allocation in cloud computing datacenters presents several research challenges, where the Virtual Machine Placement (VMP) is one of the most studied problems with several possible formulations considering a large number of existing optimization criteria. This chapter presents the main contributions that studied for the first time Many-Objective VMP (MaVMP) problems for cloud computing environments. In this context, two variants of MaVMP problems were formulated and different algorithms were designed to effectively address existing research challenges associated to the resolution of Many-Objective Optimization Problems (MaOPs). Experimental results proved the correctness of the presented algorithms, its effectiveness in solving particular associated challenges and its capabilities to solve problem instances with large numbers of physical and virtual machines for: (1) MaVMP for initial placement of VMs (static) and (2) MaVMP with reconfiguration of VMs (semi-dynamic). Finally, open research problems for the formulation and resolution of MaVMP problems for cloud computing (dynamic) are discussed.

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Notes

  1. 1.

    http://aws.amazon.com/ec2/faqs.

  2. 2.

    http://aws.amazon.com/ec2/instance-types.

  3. 3.

    https://github.com/flopezpires/iMaVMP.

  4. 4.

    https://github.com/dihara/MaVMP.

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Author information

Authors and Affiliations

  1. Itaipu Technological Park, Hernandarias, Paraguay

    Fabio López-Pires

  2. National University of Asunción, San Lorenzo, Paraguay

    Benjamín Barán

Authors
  1. Fabio López-Pires
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  2. Benjamín Barán
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Correspondence to Fabio López-Pires .

Editor information

Editors and Affiliations

  1. School of Engineering and Applied Science, Ahmedabad University, Ahmedabad, Gujarat, India

    Sanjay Chaudhary

  2. Department of Computer Science and Engineering, Central University of Rajasthan, Ajmer, Rajasthan, India

    Gaurav Somani

  3. School of Computing and Information Systems, The University of Melbourne, Melbourne, Victoria, Australia

    Rajkumar Buyya

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López-Pires, F., Barán, B. (2017). Many-Objective Optimization for Virtual Machine Placement in Cloud Computing. In: Chaudhary, S., Somani, G., Buyya, R. (eds) Research Advances in Cloud Computing. Springer, Singapore. https://doi.org/10.1007/978-981-10-5026-8_12

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  • DOI: https://doi.org/10.1007/978-981-10-5026-8_12

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  • Online ISBN: 978-981-10-5026-8

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