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Thermal-Aware Hybrid Workload Management in a Green Datacenter towards Renewable Energy Utilization

Author

Listed:
  • Yuling Li

    (State Key Laboratory of Plateau Ecology and Agriculture, Department of Computer Technology and Applications, Qinghai University, Xining 810016, China)

  • Xiaoying Wang

    (State Key Laboratory of Plateau Ecology and Agriculture, Department of Computer Technology and Applications, Qinghai University, Xining 810016, China)

  • Peicong Luo

    (State Key Laboratory of Plateau Ecology and Agriculture, Department of Computer Technology and Applications, Qinghai University, Xining 810016, China)

  • Qingyi Pan

    (State Key Laboratory of Plateau Ecology and Agriculture, Department of Computer Technology and Applications, Qinghai University, Xining 810016, China
    Department of Computer Science and Technology, Tsinghua University, Beijing 100084, China)

Abstract

The increase in massive data processing and computing in datacenters in recent years has resulted in the problem of severe energy consumption, which also leads to a significant carbon footprint and a negative impact on the environment. A growing number of IT companies with operating datacenters are adopting renewable energy as part of their energy supply to offset the consumption of brown energy. In this paper, we focused on a green datacenter using hybrid energy supply, leveraged the time flexibility of workloads in the datacenter, and proposed a thermal-aware workload management method to maximize the utilization of renewable energy sources, considering the power consumption of both computing devices and cooling devices at the same time. The critical knob of our approach was workload shifting, which scheduled more delay-tolerant workloads and allocated resources in the datacenter according to the availability of renewable energy supply and the variation of cooling temperature. In order to evaluate the performance of the proposed method, we conducted simulation experiments using the Cloudsim-plus tool. The results demonstrated that the proposed method could effectively reduce the consumption of brown energy while maximizing the utilization of green energy.

Suggested Citation

  • 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.
    • Handle: RePEc:gam:jeners:v:12:y:2019:i:8:p:1494-:d:224478
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    Citations

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    Cited by:

    1. 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.
    2. Carlos Ramos & Zita Vale & Peter Palensky & Hiroaki Nishi, 2021. "Sustainable Energy Consumption," Energies, MDPI, vol. 14(20), pages 1-3, October.
    3. Jerez Monsalves, Juan & Bergaentzlé, Claire & Keles, Dogan, 2023. "Impacts of flexible-cooling and waste-heat recovery from data centres on energy systems: A Danish case study," Energy, Elsevier, vol. 281(C).
    4. Zhou, Jing & Kanbur, Baris Burak & Le, Duc Van & Tan, Rui & Duan, Fei, 2023. "Multi-criteria assessments of increasing supply air temperature in tropical data center," Energy, Elsevier, vol. 271(C).
    5. Silva, C.A. & Vilaça, R. & Pereira, A. & Bessa, R.J., 2024. "A review on the decarbonization of high-performance computing centers," Renewable and Sustainable Energy Reviews, Elsevier, vol. 189(PB).
    6. Gupta, Rohit & Asgari, Sahar & Moazamigoodarzi, Hosein & Down, Douglas G. & Puri, Ishwar K., 2021. "Energy, exergy and computing efficiency based data center workload and cooling management," Applied Energy, Elsevier, vol. 299(C).
    7. Rostirolla, G. & Grange, L. & Minh-Thuyen, T. & Stolf, P. & Pierson, J.M. & Da Costa, G. & Baudic, G. & Haddad, M. & Kassab, A. & Nicod, J.M. & Philippe, L. & Rehn-Sonigo, V. & Roche, R. & Celik, B. &, 2022. "A survey of challenges and solutions for the integration of renewable energy in datacenters," Renewable and Sustainable Energy Reviews, Elsevier, vol. 155(C).

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