IDEAS home Printed from https://ideas.repec.org/a/gam/jsusta/v15y2023i21p15249-d1266801.html
   My bibliography  Save this article

GEECO: Green Data Centers for Energy Optimization and Carbon Footprint Reduction

Author

Listed:
  • Sudipto Mondal

    (Department of Computer Science and Engineering, United International University, Dhaka 1212, Bangladesh)

  • Fashat Bin Faruk

    (Department of Computer Science and Engineering, United International University, Dhaka 1212, Bangladesh)

  • Dibosh Rajbongshi

    (Department of Computer Science and Engineering, United International University, Dhaka 1212, Bangladesh)

  • Mohammad Masum Khondhoker Efaz

    (Department of Computer Science and Engineering, United International University, Dhaka 1212, Bangladesh)

  • Md. Motaharul Islam

    (Department of Computer Science and Engineering, United International University, Dhaka 1212, Bangladesh)

Abstract

Cloud computing has revolutionized data storage, processing, and access in modern data center operations. Conventional data centers use enormous amounts of energy for server operation, power supply, and cooling. The processors produce heat while processing the data and therefore increase the center’s carbon footprint, and the rising energy usage and carbon emissions caused by data centers pose serious environmental challenges. Under these circumstances, energy-efficient green data centers are being used as a phenomenal source of sustainable modernization. This study proposes the implementation of the Green Energy Efficiency and Carbon Optimization (GEECO) model for enhancing energy usage. Within the data center, the GEECO model dynamically adjusts workload distribution and task assignment to balance performance and manage service-level reconciliation. The ability to identify possibilities for energy optimization and carbon emission reduction is possible through real-time monitoring of energy usage and workload demand. The results revealed a considerable increase in energy efficiency, with significant decreases in energy usage and related costs. The GEECO model provides a significant improvement in energy consumption and carbon emission reduction for the different introduced scenarios. This model’s introduction to practical application would be made possible by these improvements in the quantitative results. The approach of this study also has a positive impact on the environment by reducing carbon emissions. The resilience and practicality of the solution are also analyzed, highlighting the probability of widespread adoption and its associated improvements in the advancement of sustainable cloud computing.

Suggested Citation

  • Sudipto Mondal & Fashat Bin Faruk & Dibosh Rajbongshi & Mohammad Masum Khondhoker Efaz & Md. Motaharul Islam, 2023. "GEECO: Green Data Centers for Energy Optimization and Carbon Footprint Reduction," Sustainability, MDPI, vol. 15(21), pages 1-28, October.
    • Handle: RePEc:gam:jsusta:v:15:y:2023:i:21:p:15249-:d:1266801
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2071-1050/15/21/15249/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/2071-1050/15/21/15249/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Shanchen Pang & Weiguang Zhang & Tongmao Ma & Qian Gao, 2017. "Ant Colony Optimization Algorithm to Dynamic Energy Management in Cloud Data Center," Mathematical Problems in Engineering, Hindawi, vol. 2017, pages 1-10, December.
    2. Huang, Pei & Copertaro, Benedetta & Zhang, Xingxing & Shen, Jingchun & Löfgren, Isabelle & Rönnelid, Mats & Fahlen, Jan & Andersson, Dan & Svanfeldt, Mikael, 2020. "A review of data centers as prosumers in district energy systems: Renewable energy integration and waste heat reuse for district heating," Applied Energy, Elsevier, vol. 258(C).
    3. Koot, Martijn & Wijnhoven, Fons, 2021. "Usage impact on data center electricity needs: A system dynamic forecasting model," Applied Energy, Elsevier, vol. 291(C).
    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. Li, Weiwei & Qian, Tong & Zhang, Yin & Shen, Yueqing & Wu, Chenghu & Tang, Wenhu, 2023. "Distributionally robust chance-constrained planning for regional integrated electricity–heat systems with data centers considering wind power uncertainty," Applied Energy, Elsevier, vol. 336(C).
    2. Hao, Yueting & Zhou, Haojie & Tian, Tong & Zhang, Wei & Zhou, Xin & Shen, Qingfei & Wu, Tong & Li, Ji, 2025. "Data centers waste heat recovery technologies: Review and evaluation," Applied Energy, Elsevier, vol. 384(C).
    3. Amine Bouaouda & Karim Afdel & Rachida Abounacer, 2024. "Unveiling Genetic Reinforcement Learning (GRLA) and Hybrid Attention-Enhanced Gated Recurrent Unit with Random Forest (HAGRU-RF) for Energy-Efficient Containerized Data Centers Empowered by Solar Ener," Sustainability, MDPI, vol. 16(11), pages 1-28, May.
    4. He, Wei & Ding, Su & Zhang, Jifang & Pei, Chenchen & Zhang, Zhiheng & Wang, Yulin & Li, Hailong, 2021. "Performance optimization of server water cooling system based on minimum energy consumption analysis," Applied Energy, Elsevier, vol. 303(C).
    5. Guo, Yurun & Wang, Shugang & Wang, Jihong & Zhang, Tengfei & Ma, Zhenjun & Jiang, Shuang, 2024. "Key district heating technologies for building energy flexibility: A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 189(PB).
    6. Ashouri, Mahyar & Chhokar, Callum & Bahrami, Majid, 2024. "A novel microgroove-based absorber for sorption heat transformation systems: Analytical modeling and experimental investigation," Energy, Elsevier, vol. 307(C).
    7. Babasola Osibo & Simisola Adamo, 2023. "Data Centers and Green Energy: Paving the Way for a Sustainable Digital Future," International Journal of Latest Technology in Engineering, Management & Applied Science, International Journal of Latest Technology in Engineering, Management & Applied Science (IJLTEMAS), vol. 12(11), pages 15-30, November.
    8. Du, Juntao & Shen, Zhiyang & Song, Malin & Zhang, Linda, 2023. "Nexus between digital transformation and energy technology innovation: An empirical test of A-share listed enterprises," Energy Economics, Elsevier, vol. 120(C).
    9. Han, Ouzhu & Ding, Tao & Yang, Miao & Jia, Wenhao & He, Xinran & Ma, Zhoujun, 2024. "A novel 4-level joint optimal dispatch for demand response of data centers with district autonomy realization," Applied Energy, Elsevier, vol. 358(C).
    10. Chen, Xiaoyuan & Jiang, Shan & Chen, Yu & Lei, Yi & Zhang, Donghui & Zhang, Mingshun & Gou, Huayu & Shen, Boyang, 2022. "A 10 MW class data center with ultra-dense high-efficiency energy distribution: Design and economic evaluation of superconducting DC busbar networks," Energy, Elsevier, vol. 250(C).
    11. Cho, Jinkyun, 2024. "Optimal supply air temperature with respect to data center operational stability and energy efficiency in a row-based cooling system under fault conditions," Energy, Elsevier, vol. 288(C).
    12. Golmohamadi, Hessam & Larsen, Kim Guldstrand & Jensen, Peter Gjøl & Hasrat, Imran Riaz, 2022. "Integration of flexibility potentials of district heating systems into electricity markets: A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 159(C).
    13. Guo, Yuxiang & Qu, Shengli & Wang, Chuang & Xing, Ziwen & Duan, Kaiwen, 2024. "Optimal dynamic thermal management for data center via soft actor-critic algorithm with dynamic control interval and combined-value state space," Applied Energy, Elsevier, vol. 373(C).
    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. Nord, Natasa & Shakerin, Mohammad & Tereshchenko, Tymofii & Verda, Vittorio & Borchiellini, Romano, 2021. "Data informed physical models for district heating grids with distributed heat sources to understand thermal and hydraulic aspects," Energy, Elsevier, vol. 222(C).
    16. Weijian Ding & Behzad Ebrahimi & Byoung-Do Kim & Connie L. Devenport & Amy E. Childress, 2024. "Analysis of Anthropogenic Waste Heat Emission from an Academic Data Center," Energies, MDPI, vol. 17(8), pages 1-20, April.
    17. Wang, Kaifeng & Ye, Lin & Yang, Shihui & Deng, Zhanfeng & Song, Jieying & Li, Zhuo & Zhao, Yongning, 2023. "A hierarchical dispatch strategy of hybrid energy storage system in internet data center with model predictive control," Applied Energy, Elsevier, vol. 331(C).
    18. Zehua Yu & Zheng Li & Linwei Ma, 2023. "Strategies for the Resilience of Power-Coal Supply Chains in Low-Carbon Energy Transition: A System Dynamics Model and Scenario Analysis of China up to 2060," Sustainability, MDPI, vol. 15(9), pages 1-19, April.
    19. Granda, Mariusz & Trojan, Marcin & Taler, Jan & Taler, Dawid, 2025. "Transient modelling of heat exchangers using a steady-state model for low heat capacity media," Energy, Elsevier, vol. 327(C).
    20. Mengting Jiang & Camilo Rindt & David M. J. Smeulders, 2022. "Optimal Planning of Future District Heating Systems—A Review," Energies, MDPI, vol. 15(19), pages 1-38, September.

    More about this item

    Keywords

    ;
    ;
    ;
    ;
    ;
    ;
    ;

    Statistics

    Access and download statistics

    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:jsusta:v:15:y:2023:i:21:p:15249-:d:1266801. 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.