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Profile-Based Ant Colony Optimization for Energy-Efficient Virtual Machine Placement

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Neural Information Processing (ICONIP 2017)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 10634))

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Abstract

Cloud computing data centers contain a large number of physical machines (PMs) and virtual machine (VMs). This number can increase the energy consumption of the data centers especially when the VMs placed inappropriately on the PMs. This paper presents a new VM placement approach with the objective of minimizing the total energy consumption of a data center. VM placement problem is formulated as a combinatorial optimization problem. Since this problem has been proven to be an NP hard problem, Ant Colony Optimization (ACO) algorithm is adopted to solve the formulated problem. Information heuristic of ACO is used differently based on PM energy efficiency. Experimental results show that the proposed approach scales well on large data centers and significantly outperforms selected benchmark (ACOVMP) in terms of energy consumption.

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Acknowledgment

This work is supported by Shaqra University (SU) at Saudi Arabia through the Saudi Arabian Culture Mission in Australia (SACM) (Ref No: 11954813), and the Australian Research Council (ARC) under Discovery Projects Scheme (grant no. DP170103305).

Author information

Authors and Affiliations

  1. School of Electrical Engineering and Computer Science, Queensland University of Technology, Brisbane, QLD, 4001, Australia

    Fares Alharbi, Yu-Chu Tian & Maolin Tang

  2. Department of Computing and Information Systems, The University of Melbourne, Parkville, VIC, 3010, Australia

    Md Hasanul Ferdaus

Authors
  1. Fares Alharbi
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  2. Yu-Chu Tian
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  3. Maolin Tang
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  4. Md Hasanul Ferdaus
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Corresponding author

Correspondence to Fares Alharbi .

Editor information

Editors and Affiliations

  1. Guangdong University of Technology, Guangzhou, China

    Derong Liu

  2. Guangdong University of Technology, Guangzhou, China

    Shengli Xie

  3. South China University of Technology, Guangzhou, China

    Yuanqing Li

  4. Institute of Automation, Chinese Academy of Sciences, Beijing, China

    Dongbin Zhao

  5. King Fahd University of Petroleum and Minerals, Dhahran, Saudi Arabia

    El-Sayed M. El-Alfy

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Alharbi, F., Tian, YC., Tang, M., Ferdaus, M.H. (2017). Profile-Based Ant Colony Optimization for Energy-Efficient Virtual Machine Placement. In: Liu, D., Xie, S., Li, Y., Zhao, D., El-Alfy, ES. (eds) Neural Information Processing. ICONIP 2017. Lecture Notes in Computer Science(), vol 10634. Springer, Cham. https://doi.org/10.1007/978-3-319-70087-8_88

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  • DOI: https://doi.org/10.1007/978-3-319-70087-8_88

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-70086-1

  • Online ISBN: 978-3-319-70087-8

  • eBook Packages: Computer ScienceComputer Science (R0)

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