IDEAS home Printed from https://ideas.repec.org/a/gam/jeners/v14y2021i9p2382-d541478.html
   My bibliography  Save this article

Energy-Aware Scheduling Based on Marginal Cost and Task Classification in Heterogeneous Data Centers

Author

Listed:
  • Kaixuan Ji

    (High Performance Computer Research Center, Institute of Computing Technology, Chinese Academy of Sciences, Beijing 100095, China
    School of Computer Science and Technology, University of Chinese Academy of Sciences, Beijing 101408, China
    Current Address: No.6 South Kexueyuan Rd, Beijing 100190, China.)

  • Ce Chi

    (High Performance Computer Research Center, Institute of Computing Technology, Chinese Academy of Sciences, Beijing 100095, China
    School of Computer Science and Technology, University of Chinese Academy of Sciences, Beijing 101408, China
    Current Address: No.6 South Kexueyuan Rd, Beijing 100190, China.)

  • Fa Zhang

    (High Performance Computer Research Center, Institute of Computing Technology, Chinese Academy of Sciences, Beijing 100095, China
    Current Address: No.6 South Kexueyuan Rd, Beijing 100190, China.)

  • Antonio Fernández Anta

    (IMDEA Networks Institute, Avda. del Mar Mediterraneo, 22, 28918 Leganes, Spain)

  • Penglei Song

    (Information Engineering College, Capital Normal University, Beijing 100048, China
    Current Address: No.6 South Kexueyuan Rd, Beijing 100190, China.)

  • Avinab Marahatta

    (Institute of Information Engineering, Chinese Academy of Sciences, Beijing 100093, China)

  • Youshi Wang

    (Meituan-Dianping Group, Beijing 100102, China)

  • Zhiyong Liu

    (High Performance Computer Research Center, Institute of Computing Technology, Chinese Academy of Sciences, Beijing 100095, China
    Current Address: No.6 South Kexueyuan Rd, Beijing 100190, China.)

Abstract

The energy consumption problem has become a bottleneck hindering further development of data centers. However, the heterogeneity of servers, hybrid cooling modes, and extra energy caused by system state transitions increases the complexity of the energy optimization problem. To deal with such challenges, in this paper, an Energy Aware Task Scheduling strategy (EATS) utilizing marginal cost and task classification method is proposed that cooperatively improves the energy efficiency of servers and cooling systems. An energy consumption model for servers, cooling systems, and state transition is developed, and the energy optimization problem in data centers is formulated. The concept of marginal cost is introduced to guide the task scheduling process. The task classification method is incorporated with the idea of marginal cost to further improve resource utilization and reduce the total energy consumption of data centers. Experiments are conducted using real-world traces, and energy reduction results are compared. Results show that EATS achieves more energy-savings of servers, cooling systems, state transition in comparison to the other two techniques under a various number of servers, cooling modules and task arrival intensities. It is validated that EATS is effective at reducing total energy consumption and improving the resource utilization of data centers.

Suggested Citation

  • Kaixuan Ji & Ce Chi & Fa Zhang & Antonio Fernández Anta & Penglei Song & Avinab Marahatta & Youshi Wang & Zhiyong Liu, 2021. "Energy-Aware Scheduling Based on Marginal Cost and Task Classification in Heterogeneous Data Centers," Energies, MDPI, vol. 14(9), pages 1-26, April.
    • Handle: RePEc:gam:jeners:v:14:y:2021:i:9:p:2382-:d:541478
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1996-1073/14/9/2382/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/1996-1073/14/9/2382/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    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. Moazamigoodarzi, Hosein & Tsai, Peiying Jennifer & Pal, Souvik & Ghosh, Suvojit & Puri, Ishwar K., 2019. "Influence of cooling architecture on data center power consumption," Energy, Elsevier, vol. 183(C), pages 525-535.
    3. Nicola Jones, 2018. "How to stop data centres from gobbling up the world’s electricity," Nature, Nature, vol. 561(7722), pages 163-166, September.
    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. Ana Salomé García-Muñiz & María Rosalía Vicente, 2021. "The Effects of Informational Feedback on the Energy Consumption of Online Services: Some Evidence for the European Union," Energies, MDPI, vol. 14(10), pages 1-14, May.
    2. Fridgen, Gilbert & Keller, Robert & Körner, Marc-Fabian & Schöpf, Michael, 2020. "A holistic view on sector coupling," Energy Policy, Elsevier, vol. 147(C).
    3. Muhammad Fahad & Arsalan Shahid & Ravi Reddy Manumachu & Alexey Lastovetsky, 2019. "A Comparative Study of Methods for Measurement of Energy of Computing," Energies, MDPI, vol. 12(11), pages 1-42, June.
    4. John Martinovic & Markus Hähnel & Guntram Scheithauer & Waltenegus Dargie, 2022. "An introduction to stochastic bin packing-based server consolidation with conflicts," TOP: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 30(2), pages 296-331, July.
    5. Nguyen, Quyen & Diaz-Rainey, Ivan & Kuruppuarachchi, Duminda, 2021. "Predicting corporate carbon footprints for climate finance risk analyses: A machine learning approach," Energy Economics, Elsevier, vol. 95(C).
    6. Salil Bharany & Sandeep Sharma & Osamah Ibrahim Khalaf & Ghaida Muttashar Abdulsahib & Abeer S. Al Humaimeedy & Theyazn H. H. Aldhyani & Mashael Maashi & Hasan Alkahtani, 2022. "A Systematic Survey on Energy-Efficient Techniques in Sustainable Cloud Computing," Sustainability, MDPI, vol. 14(10), pages 1-89, May.
    7. Jose Loyola-Fuentes & Luca Pietrasanta & Marco Marengo & Francesco Coletti, 2022. "Machine Learning Algorithms for Flow Pattern Classification in Pulsating Heat Pipes," Energies, MDPI, vol. 15(6), pages 1-20, March.
    8. Evgeny Burnaev & Evgeny Mironov & Aleksei Shpilman & Maxim Mironenko & Dmitry Katalevsky, 2023. "Practical AI Cases for Solving ESG Challenges," Sustainability, MDPI, vol. 15(17), pages 1-15, August.
    9. Gupta, Rohit & Moazamigoodarzi, Hosein & MirhoseiniNejad, SeyedMorteza & Down, Douglas G. & Puri, Ishwar K., 2020. "Workload management for air-cooled data centers: An energy and exergy based approach," Energy, Elsevier, vol. 209(C).
    10. Ye, Fei & Ouyang, You & Li, Yina, 2023. "Digital investment and environmental performance: The mediating roles of production efficiency and green innovation," International Journal of Production Economics, Elsevier, vol. 259(C).
    11. 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).
    12. Chen, Sirui & Li, Peng & Ji, Haoran & Yu, Hao & Yan, Jinyue & Wu, Jianzhong & Wang, Chengshan, 2021. "Operational flexibility of active distribution networks with the potential from data centers," Applied Energy, Elsevier, vol. 293(C).
    13. Mengmeng Zhao & Xiaoying Wang, 2021. "A Synthetic Approach for Datacenter Power Consumption Regulation towards Specific Targets in Smart Grid Environment," Energies, MDPI, vol. 14(9), pages 1-25, May.
    14. Hu, Zhi-Hua & Zheng, Yu-Xin & Wang, You-Gan, 2022. "Packing computing servers into the vessel of an underwater data center considering cooling efficiency," Applied Energy, Elsevier, vol. 314(C).
    15. Dwivedi, Yogesh K. & Hughes, Laurie & Kar, Arpan Kumar & Baabdullah, Abdullah M. & Grover, Purva & Abbas, Roba & Andreini, Daniela & Abumoghli, Iyad & Barlette, Yves & Bunker, Deborah & Chandra Kruse,, 2022. "Climate change and COP26: Are digital technologies and information management part of the problem or the solution? An editorial reflection and call to action," International Journal of Information Management, Elsevier, vol. 63(C).
    16. Gilbert Fridgen & Marc-Fabian Körner & Steffen Walters & Martin Weibelzahl, 2021. "Not All Doom and Gloom: How Energy-Intensive and Temporally Flexible Data Center Applications May Actually Promote Renewable Energy Sources," Business & Information Systems Engineering: The International Journal of WIRTSCHAFTSINFORMATIK, Springer;Gesellschaft für Informatik e.V. (GI), vol. 63(3), pages 243-256, June.
    17. Han, Zongwei & Ji, Qiang & Wei, Haotian & Xue, Da & Sun, Xiaoqing & Zhang, Xueping & Li, Xiuming, 2020. "Simulation study on performance of data center air-conditioning system with novel evaporative condenser," Energy, Elsevier, vol. 210(C).
    18. Ce Chi & Kaixuan Ji & Penglei Song & Avinab Marahatta & Shikui Zhang & Fa Zhang & Dehui Qiu & Zhiyong Liu, 2021. "Cooperatively Improving Data Center Energy Efficiency Based on Multi-Agent Deep Reinforcement Learning," Energies, MDPI, vol. 14(8), pages 1-32, April.
    19. Etienne Romsom & Kathryn McPhail, 2020. "The energy transition in Asia: Country priorities, fuel types, and energy decisions," WIDER Working Paper Series wp-2020-48, World Institute for Development Economic Research (UNU-WIDER).
    20. Gupta, Rohit & Asgari, Sahar & Moazamigoodarzi, Hosein & Pal, Souvik & Puri, Ishwar K., 2020. "Cooling architecture selection for air-cooled Data Centers by minimizing exergy destruction," Energy, Elsevier, vol. 201(C).

    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:14:y:2021:i:9:p:2382-:d:541478. 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.