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Variable Neighborhood Search-Based Symbiotic Organisms Search Algorithm for Energy-Efficient Scheduling of Virtual Machine in Cloud Data Center

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Advances on Computational Intelligence in Energy

Part of the book series: Green Energy and Technology ((GREEN))

Abstract

The quest for energy-efficient virtual machine placement algorithms has attracted significant attention of researchers in the cloud computing platform. This paper applied a novel symbiotic organisms search (SOS) algorithm to minimize the number of active server by consolidation VMs on few servers for energy savings. SOS algorithm was inspired by symbiotic relationship exhibit by organisms in an ecosystem to boost their chances of survival. Essentially, SOS mimics mutualism, commensalism, and parasitism forms of relationship for traversing the search space. Hybridized with variable neighborhood search, the hybrid algorithm is termed SOS-VNS. SOS-VNS algorithm is efficient in minimizing energy consumption and improving resource utilization. The SOS-VNS algorithm is applied to various workload instances with varying number of VMs in a simulated IaaS cloud. The results obtained showed that SOS-VNS outperforms the heuristics and achieved reasonable energy savings while improving resource utilization.

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Author information

Authors and Affiliations

  1. Department of Computer Science, Ahmadu Bello University, Zaria, Nigeria

    Mohammed Abdullahi & Salihu Idi Dishing

  2. Department of Cyber Security Science, Federal University of Technology Minna, Minna, Niger, Nigeria

    Shafi’i Muhammad Abdulhamid

  3. Department of Mathematics, Bauchi State University Gadau, Gadau, PMB 068, Bauchi, Nigeria

    Mohammed Joda Usman

Authors
  1. Mohammed Abdullahi
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  2. Shafi’i Muhammad Abdulhamid
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  3. Salihu Idi Dishing
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  4. Mohammed Joda Usman
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Corresponding author

Correspondence to Shafi’i Muhammad Abdulhamid .

Editor information

Editors and Affiliations

  1. University of Malaya, Kuala Lumpur, Malaysia

    Tutut Herawan

  2. Federal College of Education (Technical), Gombe, Nigeria

    Haruna Chiroma

  3. Deakin University, Geelong, VIC, Australia

    Jemal H. Abawajy

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Abdullahi, M., Abdulhamid, S.M., Dishing, S.I., Usman, M.J. (2019). Variable Neighborhood Search-Based Symbiotic Organisms Search Algorithm for Energy-Efficient Scheduling of Virtual Machine in Cloud Data Center. In: Herawan, T., Chiroma, H., Abawajy, J. (eds) Advances on Computational Intelligence in Energy. Green Energy and Technology. Springer, Cham. https://doi.org/10.1007/978-3-319-69889-2_5

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  • DOI: https://doi.org/10.1007/978-3-319-69889-2_5

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-69888-5

  • Online ISBN: 978-3-319-69889-2

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