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Minimal Green Energy Consumption and Workload Management for Data Centers on Smart City Platforms

Author

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  • Pei Pei

    (School of Economics and Management, Lanzhou University of Technology, Lanzhou 730050, China)

  • Zongjie Huo

    (School of Economics and Management, Lanzhou University of Technology, Lanzhou 730050, China)

  • Oscar Sanjuán Martínez

    (Computer Science Department, School of Engineering and Technology, Universidad Internacional de La Rioja, 26006 Logroño, La Rioja, Spain)

  • Rubén González Crespo

    (Computer Science Department, School of Engineering and Technology, Universidad Internacional de La Rioja, 26006 Logroño, La Rioja, Spain)

Abstract

Presently, energy is considered a significant resource that grows scarce with high demand and population in the global market. Therefore, a survey suggested that renewable energy sources are required to avoid scarcity. Hence, in this paper, a smart, sustainable probability distribution hybridized genetic approach (SSPD-HG) has been proposed to decrease energy consumption and minimize the total completion time for a single machine in smart city machine interface platforms. Further, the estimated set of non-dominated alternative using a multi-objective genetic algorithm has been hybridized to address the problem, which is mathematically computed in this research. This paper discusses the need to promote the integration of green energy to reduce energy use costs by balancing regional loads. Further, the timely production of delay-tolerant working loads and the management of thermal storage at data centers has been analyzed in this research. In addition, differences in bandwidth rates between users and data centers are taken into account and analyzed at a lab scale using SSPD-HG for energy-saving costs and managing a balanced workload.

Suggested Citation

  • Pei Pei & Zongjie Huo & Oscar Sanjuán Martínez & Rubén González Crespo, 2020. "Minimal Green Energy Consumption and Workload Management for Data Centers on Smart City Platforms," Sustainability, MDPI, vol. 12(8), pages 1-14, April.
    • Handle: RePEc:gam:jsusta:v:12:y:2020:i:8:p:3140-:d:345222
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    References listed on IDEAS

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    1. Shiann Ming Wu & Tsung-chun Chen & Yenchun Jim Wu & Miltiadis Lytras, 2018. "Smart Cities in Taiwan: A Perspective on Big Data Applications," Sustainability, MDPI, vol. 10(1), pages 1-14, January.
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    Cited by:

    1. Tatiana Tucunduva Philippi Cortese & Jairo Filho Sousa de Almeida & Giseli Quirino Batista & José Eduardo Storopoli & Aaron Liu & Tan Yigitcanlar, 2022. "Understanding Sustainable Energy in the Context of Smart Cities: A PRISMA Review," Energies, MDPI, vol. 15(7), pages 1-38, March.
    2. Xihao Wang & Xiaojun Wang & Yuqing Liu & Chun Xiao & Rongsheng Zhao & Ye Yang & Zhao Liu, 2022. "A Sustainability Improvement Strategy of Interconnected Data Centers Based on Dispatching Potential of Electric Vehicle Charging Stations," Sustainability, MDPI, vol. 14(11), pages 1-19, June.
    3. Tudor Cioara & Marcel Antal & Claudia Daniela Antal (Pop) & Ionut Anghel & Massimo Bertoncini & Diego Arnone & Marilena Lazzaro & Marzia Mammina & Terpsichori-Helen Velivassaki & Artemis Voulkidis & Y, 2020. "Data Centers Optimized Integration with Multi-Energy Grids: Test Cases and Results in Operational Environment," Sustainability, MDPI, vol. 12(23), pages 1-23, November.
    4. Jesús M. Blanco & Yokasta García Frómeta & Maggi Madrid & Jesús Cuadrado, 2021. "Thermal Performance Assessment of Walls Made of Three Types of Sustainable Concrete Blocks by Means of FEM and Validated through an Extensive Measurement Campaign," Sustainability, MDPI, vol. 13(1), pages 1-18, January.

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