Cloud Computing. 6th International Conference, CloudComp 2015, Daejeon, South Korea, October 28-29, 2015, Proceedings

Research Article

Dynamic Virtual Machine Consolidation for Energy Efficient Cloud Data Centers

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  • @INPROCEEDINGS{10.1007/978-3-319-38904-2_8,
        author={Dong-Ki Kang and Fawaz Alhazemi and Seong-Hwan Kim and Chan-Hyun Youn},
        title={Dynamic Virtual Machine Consolidation for Energy Efficient Cloud Data Centers},
        proceedings={Cloud Computing. 6th International Conference, CloudComp 2015, Daejeon, South Korea, October 28-29, 2015, Proceedings},
        proceedings_a={CLOUDCOMP},
        year={2016},
        month={5},
        keywords={Cloud computing Virtual Machine migration Dynamic Right Sizing Workload Prediction},
        doi={10.1007/978-3-319-38904-2_8}
    }
    
  • Dong-Ki Kang
    Fawaz Alhazemi
    Seong-Hwan Kim
    Chan-Hyun Youn
    Year: 2016
    Dynamic Virtual Machine Consolidation for Energy Efficient Cloud Data Centers
    CLOUDCOMP
    Springer
    DOI: 10.1007/978-3-319-38904-2_8
Dong-Ki Kang1,*, Fawaz Alhazemi1,*, Seong-Hwan Kim1,*, Chan-Hyun Youn1,*
  • 1: KAIST
*Contact email: dkkang@kaist.ac.kr, fawaz@kaist.ac.kr, s.h_kim@kaist.ac.kr, chyoun@kaist.ac.kr

Abstract

As a cloud computing model have led clusters to the large-scale data centers, reducing of the energy consumption which imposes a crucial part of the whole operating expense for data centers has received a lot of attention of a wide public. At cluster-level viewpoint, the most popular method for energy efficient cloud is Dynamic Right Sizing (DRS), which turns off idle servers those do not have any of running virtual resources. To maximize the energy efficiency through DRS, one of primary adaptive resource management strategies is a Virtual Machine (VM) consolidation which integrates VM instances into as few servers as possible. In this paper, we propose Virtual machine Consolidation based Size Decision (VC-SD) approach migrates VM instances from under-utilized servers which are supposed to be turned off to sustaining ones according to their monitored resource utilizations in real time. In addition, we design a Self Adjusting Workload Prediction (SAWP) method to improve a forecasting accuracy of resource utilization even under irregular demand patterns. Through experimental results based on real cloud servers, we show various metrics such as resource utilization, energy consumption and switching overhead caused by application processing, VM migration and DRS execution to verify a necessity of our proposed methodologies.