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Thermal-Aware Virtual Machine Allocation for Heterogeneous Cloud Data Centers

Author

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  • Abbas Akbari

    (School of Electrical and Computer Engineering, University of Tehran, Tehran 146899-5513, Iran)

  • Ahmad Khonsari

    (School of Electrical and Computer Engineering, University of Tehran, Tehran 146899-5513, Iran)

  • Seyed Mohammad Ghoreyshi

    (School of Mathematical Sciences, University of Southampton, Southampton SO14 0AB, UK)

Abstract

In recent years, a large and growing body of literature has addressed the energy-efficient resource management problem in data centers. Due to the fact that cooling costs still remain the major portion of the total data center energy cost, thermal-aware resource management techniques have been employed to make additional energy savings. In this paper, we formulate the problem of minimizing the total energy consumption of a heterogeneous data center (MITEC) as a non-linear integer optimization problem. We consider both computing and cooling energy consumption and provide a thermal-aware Virtual Machine (VM) allocation heuristic based on the genetic algorithm. Experimental results show that, using the proposed formulation, up to 30 % energy saving is achieved compared to thermal-aware greedy algorithms and power-aware VM allocation heuristics.

Suggested Citation

  • Abbas Akbari & Ahmad Khonsari & Seyed Mohammad Ghoreyshi, 2020. "Thermal-Aware Virtual Machine Allocation for Heterogeneous Cloud Data Centers," Energies, MDPI, vol. 13(11), pages 1-15, June.
  • Handle: RePEc:gam:jeners:v:13:y:2020:i:11:p:2880-:d:367516
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    References listed on IDEAS

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    1. Maria Avgerinou & Paolo Bertoldi & Luca Castellazzi, 2017. "Trends in Data Centre Energy Consumption under the European Code of Conduct for Data Centre Energy Efficiency," Energies, MDPI, vol. 10(10), pages 1-18, September.
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    4. Yuling Li & Xiaoying Wang & Peicong Luo & Qingyi Pan, 2019. "Thermal-Aware Hybrid Workload Management in a Green Datacenter towards Renewable Energy Utilization," Energies, MDPI, vol. 12(8), pages 1-18, April.
    5. Shuja, Junaid & Gani, Abdullah & Shamshirband, Shahaboddin & Ahmad, Raja Wasim & Bilal, Kashif, 2016. "Sustainable Cloud Data Centers: A survey of enabling techniques and technologies," Renewable and Sustainable Energy Reviews, Elsevier, vol. 62(C), pages 195-214.
    6. Yan Bai & Lijun Gu, 2017. "Chip Temperature-Based Workload Allocation for Holistic Power Minimization in Air-Cooled Data Center," Energies, MDPI, vol. 10(12), pages 1-19, December.
    7. Robert Basmadjian, 2019. "Flexibility-Based Energy and Demand Management in Data Centers: A Case Study for Cloud Computing," Energies, MDPI, vol. 12(17), pages 1-22, August.
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    Cited by:

    1. Kaixuan Ji & Ce Chi & Fa Zhang & Antonio Fernández Anta & Penglei Song & Avinab Marahatta & Youshi Wang & Zhiyong Liu, 2021. "Energy-Aware Scheduling Based on Marginal Cost and Task Classification in Heterogeneous Data Centers," Energies, MDPI, vol. 14(9), pages 1-26, April.
    2. Mengmeng Zhao & Xiaoying Wang, 2021. "A Synthetic Approach for Datacenter Power Consumption Regulation towards Specific Targets in Smart Grid Environment," Energies, MDPI, vol. 14(9), pages 1-25, May.

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