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An Integrated Modeling Approach to Evaluate and Optimize Data Center Sustainability, Dependability and Cost

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  • Gustavo Callou

    (Informatics Center, Federal University of Pernambuco, Av. Jornalista Anibal Fernandes, s/n, Cidade Universitária, Recife 50740-560, Brazil
    Department of Statistics and Informatics, Federal Rural University of Pernambuco, Rua Dom Manoel de Medeiros, s/n. Campus Dois Irmãos, Recife 52171-900, Brazil)

  • João Ferreira

    (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)

  • Dietmar Tutsch

    (Automation/Computer Science, University of Wuppertal, Bldg. FC.2.12 Rainer-Gruenter-Str. 21, Wuppertal 42119, Germany)

  • Rafael Souza

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

Abstract

Data centers have evolved dramatically in recent years, due to the advent of social networking services, e-commerce and cloud computing. The conflicting requirements are the high availability levels demanded against the low sustainability impact and cost values. The approaches that evaluate and optimize these requirements are essential to support designers of data center architectures. Our work aims to propose an integrated approach to estimate and optimize these issues with the support of the developed environment, Mercury. Mercury is a tool for dependability, performance and energy flow evaluation. The tool supports reliability block diagrams (RBD), stochastic Petri nets (SPNs), continuous-time Markov chains (CTMC) and energy flow (EFM) models. The EFM verifies the energy flow on data center architectures, taking into account the energy efficiency and power capacity that each device can provide (assuming power systems) or extract (considering cooling components). The EFM also estimates the sustainability impact and cost issues of data center architectures. Additionally, a methodology is also considered to support the modeling, evaluation and optimization processes. Two case studies are presented to illustrate the adopted methodology on data center power systems.

Suggested Citation

  • 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.
    • Handle: RePEc:gam:jeners:v:7:y:2014:i:1:p:238-277:d:31991
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    References listed on IDEAS

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    1. Rosen, Marc A. & Dincer, Ibrahim & Kanoglu, Mehmet, 2008. "Role of exergy in increasing efficiency and sustainability and reducing environmental impact," Energy Policy, Elsevier, vol. 36(1), pages 128-137, January.
    2. Kanoglu, Mehmet & Dincer, Ibrahim & Rosen, Marc A., 2007. "Understanding energy and exergy efficiencies for improved energy management in power plants," Energy Policy, Elsevier, vol. 35(7), pages 3967-3978, July.
    3. 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.
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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. Bennaceur, Walid Mokhtar & Kloul, Leïla, 2020. "Formal models for safety and performance analysis of a data center system," Reliability Engineering and System Safety, Elsevier, vol. 193(C).
    3. Li, Xiao-Yang & Liu, Yue & Lin, Yan-Hui & Xiao, Liang-Hua & Zio, Enrico & Kang, Rui, 2021. "A generalized petri net-based modeling framework for service reliability evaluation and management of cloud data centers," Reliability Engineering and System Safety, Elsevier, vol. 207(C).
    4. Xiao-Fang Liu & Zhi-Hui Zhan & Jun Zhang, 2017. "An Energy Aware Unified Ant Colony System for Dynamic Virtual Machine Placement in Cloud Computing," Energies, MDPI, vol. 10(5), pages 1-15, May.
    5. 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.
    6. Jose Alejandro Cano & Abraham Londoño-Pineda & Maria Fanny Castro & Hugo Bécquer Paz & Carolina Rodas & Tatiana Arias, 2022. "A Bibliometric Analysis and Systematic Review on E-Marketplaces, Open Innovation, and Sustainability," Sustainability, MDPI, vol. 14(9), pages 1-42, May.

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