IDEAS home Printed from https://ideas.repec.org/a/gam/jeners/v12y2019i11p2129-d236926.html
   My bibliography  Save this article

Eco-Efficient Resource Management in HPC Clusters through Computer Intelligence Techniques

Author

Listed:
  • Alberto Cocaña-Fernández

    (Departamento de Informática, Universidad de Oviedo, 33204 Gijón, Spain)

  • Emilio San José Guiote

    (Departamento de Informática, Universidad de Oviedo, 33204 Gijón, Spain)

  • Luciano Sánchez

    (Departamento de Informática, Universidad de Oviedo, 33204 Gijón, Spain)

  • José Ranilla

    (Departamento de Informática, Universidad de Oviedo, 33204 Gijón, Spain)

Abstract

High Performance Computing Clusters (HPCCs) are common platforms for solving both up-to-date challenges and high-dimensional problems faced by IT service providers. Nonetheless, the use of HPCCs carries a substantial and growing economic and environmental impact, owing to the large amount of energy they need to operate. In this paper, a two-stage holistic optimisation mechanism is proposed to manage HPCCs in an eco-efficiently manner. The first stage logically optimises the resources of the HPCC through reactive and proactive strategies, while the second stage optimises hardware allocation by leveraging a genetic fuzzy system tailored to the underlying equipment. The model finds optimal trade-offs among quality of service, direct/indirect operating costs, and environmental impact, through multiobjective evolutionary algorithms meeting the preferences of the administrator. Experimentation was done using both actual workloads from the Scientific Modelling Cluster of the University of Oviedo and synthetically-generated workloads, showing statistical evidence supporting the adoption of the new mechanism.

Suggested Citation

  • Alberto Cocaña-Fernández & Emilio San José Guiote & Luciano Sánchez & José Ranilla, 2019. "Eco-Efficient Resource Management in HPC Clusters through Computer Intelligence Techniques," Energies, MDPI, vol. 12(11), pages 1-21, June.
    • Handle: RePEc:gam:jeners:v:12:y:2019:i:11:p:2129-:d:236926
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1996-1073/12/11/2129/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/1996-1073/12/11/2129/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Alberto Cocaña-Fernández & Luciano Sánchez & José Ranilla, 2016. "Improving the Eco-Efficiency of High Performance Computing Clusters Using EECluster," Energies, MDPI, vol. 9(3), pages 1-16, March.
    2. 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.
    3. Ni, Jiacheng & Bai, Xuelian, 2017. "A review of air conditioning energy performance in data centers," Renewable and Sustainable Energy Reviews, Elsevier, vol. 67(C), pages 625-640.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Leehter Yao & Jin-Hao Huang, 2019. "Multi-Objective Optimization of Energy Saving Control for Air Conditioning System in Data Center," Energies, MDPI, vol. 12(8), pages 1-16, April.
    2. DeLovato, Nicolas & Sundarnath, Kavin & Cvijovic, Lazar & Kota, Krishna & Kuravi, Sarada, 2019. "A review of heat recovery applications for solar and geothermal power plants," Renewable and Sustainable Energy Reviews, Elsevier, vol. 114(C), pages 1-1.
    3. Lange, Steffen & Pohl, Johanna & Santarius, Tilman, 2020. "Digitalization and energy consumption. Does ICT reduce energy demand?," Ecological Economics, Elsevier, vol. 176(C).
    4. Wang, Fengjuan & Lv, Chengwei & Xu, Jiuping, 2023. "Carbon awareness oriented data center location and configuration: An integrated optimization method," Energy, Elsevier, vol. 278(C).
    5. Shunling Ruan & Haiyan Xie & Song Jiang, 2017. "Integrated Proactive Control Model for Energy Efficiency Processes in Facilities Management: Applying Dynamic Exponential Smoothing Optimization," Sustainability, MDPI, vol. 9(9), pages 1-22, September.
    6. van den Berg, Bob & Sadowski, Bert M. & Pals, Luuk, 2018. "Towards sustainable data centres: Novel internal network technologies leading to sustainable cost and energy consumption in data centres in The Netherlands," 29th European Regional ITS Conference, Trento 2018 184933, International Telecommunications Society (ITS).
    7. Hackl, Andreas, 2018. "Mobility equity in a globalized world: Reducing inequalities in the sustainable development agenda," World Development, Elsevier, vol. 112(C), pages 150-162.
    8. Madhubala Ganesan & Ah-Lian Kor & Colin Pattinson & Eric Rondeau, 2020. "Green Cloud Software Engineering for Big Data Processing," Sustainability, MDPI, vol. 12(21), pages 1-24, November.
    9. Anagnostos, D. & Schmidt, T. & Cavadias, S. & Soudris, D. & Poortmans, J. & Catthoor, F., 2019. "A method for detailed, short-term energy yield forecasting of photovoltaic installations," Renewable Energy, Elsevier, vol. 130(C), pages 122-129.
    10. Borkowski, Mateusz & Piłat, Adam Krzysztof, 2022. "Customized data center cooling system operating at significant outdoor temperature fluctuations," Applied Energy, Elsevier, vol. 306(PB).
    11. HOROBEȚ Alexandra & MNOHOGHITNEI Irina & BELAȘCU Lucian & CROITORU Ionuț Marius, 2023. "Esg Reporting And Capital Market Investors: Insights From The Global Technology And Fintech Industries," Studies in Business and Economics, Lucian Blaga University of Sibiu, Faculty of Economic Sciences, vol. 18(2), pages 178-195, August.
    12. Boza, Pal & Evgeniou, Theodoros, 2021. "Artificial intelligence to support the integration of variable renewable energy sources to the power system," Applied Energy, Elsevier, vol. 290(C).
    13. Jing Ni & Bowen Jin & Bo Zhang & Xiaowei Wang, 2017. "Simulation of Thermal Distribution and Airflow for Efficient Energy Consumption in a Small Data Centers," Sustainability, MDPI, vol. 9(4), pages 1-16, April.
    14. 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).
    15. Pio Alessandro Lombardi & Kranthi Ranadheer Moreddy & André Naumann & Przemyslaw Komarnicki & Carmine Rodio & Sergio Bruno, 2019. "Data Centers as Active Multi-Energy Systems for Power Grid Decarbonization: A Technical and Economic Analysis," Energies, MDPI, vol. 12(21), pages 1-14, November.
    16. M. Hasan Jamal & M. Tayyab Chaudhry & Usama Tahir & Furqan Rustam & Soojung Hur & Imran Ashraf, 2022. "Hotspot-Aware Workload Scheduling and Server Placement for Heterogeneous Cloud Data Centers," Energies, MDPI, vol. 15(7), pages 1-20, March.
    17. Tong, Zhen & Wang, Wencheng & Fang, Chunxue, 2023. "Energy-saving potential analysis of a CO2 two-phase thermosyphon loop system used in data centers," Energy, Elsevier, vol. 275(C).
    18. Ye, Guisen & Gao, Feng & Fang, Jingyang, 2022. "A mission-driven two-step virtual machine commitment for energy saving of modern data centers through UPS and server coordinated optimizations," Applied Energy, Elsevier, vol. 322(C).
    19. Jin, Chaoqiang & Bai, Xuelian & Yang, Chao & Mao, Wangxin & Xu, Xin, 2020. "A review of power consumption models of servers in data centers," Applied Energy, Elsevier, vol. 265(C).
    20. Wafaa Saleh & Shekaina Justin & Ghada Alsawah & Tasneem Al Ghamdi & Maha M. A. Lashin, 2021. "Control Strategies for Energy Efficiency at PNU’s Metro System," Energies, MDPI, vol. 14(20), pages 1-13, October.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jeners:v:12:y:2019:i:11:p:2129-:d:236926. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.