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A Power Load Distribution Algorithm to Optimize Data Center Electrical Flow

Author

Listed:
  • João Ferreira

    (Informatics Center, Federal University of Pernambuco, Av. Jornalista Anibal Fernandes, s/n, Cidade Universitária, Recife 50740-560, Brazil)

  • Gustavo Callou

    (Informatics Center, Federal University of Pernambuco, Av. Jornalista Anibal Fernandes, s/n, Cidade Universitária, Recife 50740-560, Brazil)

  • Paulo Maciel

    (Informatics Center, Federal University of Pernambuco, Av. Jornalista Anibal Fernandes, s/n, Cidade Universitária, Recife 50740-560, Brazil)

Abstract

Energy consumption is a matter of common concern in the world today. Research demonstrates that as a consequence of the constantly evolving and expanding field of information technology, data centers are now major consumers of electrical energy. Such high electrical energy consumption emphasizes the issues of sustainability and cost. Against this background, the present paper proposes a power load distribution algorithm (PLDA) to optimize energy distribution of data center power infrastructures. The PLDA, which is based on the Ford-Fulkerson algorithm, is supported by an environment called ASTRO, capable of performing the integrated evaluation of dependability, cost and sustainability. More specifically, the PLDA optimizes the flow distribution of the energy flow model (EFM). EFMs are responsible for estimating sustainability and cost issues of data center infrastructures without crossing the restrictions of the power capacity that each device can provide (power system) or extract (cooling system). Additionally, a case study is presented that analyzed seven data center power architectures. Significant results were observed, achieving a reduction in power consumption of up to 15.5%.

Suggested Citation

  • João Ferreira & Gustavo Callou & Paulo Maciel, 2013. "A Power Load Distribution Algorithm to Optimize Data Center Electrical Flow," Energies, MDPI, vol. 6(7), pages 1-22, July.
    • Handle: RePEc:gam:jeners:v:6:y:2013:i:7:p:3422-3443:d:27211
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    References listed on IDEAS

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    1. Baños, R. & Manzano-Agugliaro, F. & Montoya, F.G. & Gil, C. & Alcayde, A. & Gómez, J., 2011. "Optimization methods applied to renewable and sustainable energy: A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 15(4), pages 1753-1766, May.
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    Cited by:

    1. Syed Naeem Haider & Qianchuan Zhao & Xueliang Li, 2020. "Cluster-Based Prediction for Batteries in Data Centers," Energies, MDPI, vol. 13(5), pages 1-17, March.
    2. Gustavo Callou & João Ferreira & Paulo Maciel & Dietmar Tutsch & Rafael Souza, 2014. "An Integrated Modeling Approach to Evaluate and Optimize Data Center Sustainability, Dependability and Cost," Energies, MDPI, vol. 7(1), pages 1-40, January.
    3. Joao Ferreira & Gustavo Callou & Dietmar Tutsch & Paulo Maciel, 2018. "PLDAD—An Algorihm to Reduce Data Center Energy Consumption," Energies, MDPI, vol. 11(10), pages 1-24, October.

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