Cloud Computing. Third International Conference, CloudComp 2012, Vienna, Austria, September 24-26, 2012, Revised Selected Papers

Research Article

Design and Implementation of a Multi-objective Optimization Mechanism for Virtual Machine Placement in Cloud Computing Data Center

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  • @INPROCEEDINGS{10.1007/978-3-319-03874-2_3,
        author={Soichi Shigeta and Hiroyuki Yamashima and Tsunehisa Doi and Tsutomu Kawai and Keisuke Fukui},
        title={Design and Implementation of a Multi-objective Optimization Mechanism for Virtual Machine Placement in Cloud Computing Data Center},
        proceedings={Cloud Computing. Third International Conference, CloudComp 2012, Vienna, Austria, September 24-26, 2012, Revised Selected Papers},
        proceedings_a={CLOUDCOMP},
        year={2014},
        month={6},
        keywords={cloud computing VM placement multi-objective optimization},
        doi={10.1007/978-3-319-03874-2_3}
    }
    
  • Soichi Shigeta
    Hiroyuki Yamashima
    Tsunehisa Doi
    Tsutomu Kawai
    Keisuke Fukui
    Year: 2014
    Design and Implementation of a Multi-objective Optimization Mechanism for Virtual Machine Placement in Cloud Computing Data Center
    CLOUDCOMP
    Springer
    DOI: 10.1007/978-3-319-03874-2_3
Soichi Shigeta1,*, Hiroyuki Yamashima1,*, Tsunehisa Doi1,*, Tsutomu Kawai1,*, Keisuke Fukui1,*
  • 1: Fujitsu Laboratories Ltd.
*Contact email: shigets@labs.fujitsu.com, yama@labs.fujitsu.com, micky@labs.fujitsu.com, tkawai@labs.fujitsu.com, kfukui@labs.fujitsu.com

Abstract

Cloud computing is becoming a popular way of supplying and using computing resources. A cloud-computing data center is equipped with a large number of physical resources and must manage an even larger number of virtual machines (VMs). The center’s VM placement strategy affects the utilization of physical resources, and consequently, it influences operational costs. Our goal is to develop a multi-objective optimization mechanism for VM placement that satisfies various constraints and results in the lowest operational cost. The number of possible combinations of VMs and hosts can be extremely large. For the mechanism to be practical, the number of possible combinations must be reduced. We reduced computational overheads by classifying VM hosts into a relatively small number of equivalent sets. Simulation results show that expected operational costs can be significantly reduced by applying the proposed mechanism.