IDEAS home Printed from https://ideas.repec.org/a/gam/jftint/v17y2025i6p261-d1678902.html
   My bibliography  Save this article

Network-, Cost-, and Renewable-Aware Ant Colony Optimization for Energy-Efficient Virtual Machine Placement in Cloud Datacenters

Author

Listed:
  • Ali Mohammad Baydoun

    (Department of Mathematics & Computer Science, Beirut Arab University, Beirut 1107, Lebanon)

  • Ahmed Sherif Zekri

    (Department of Mathematics & Computer Science, Alexandria University, Alexandria 21526, Egypt)

Abstract

Virtual machine (VM) placement in cloud datacenters is a complex multi-objective challenge involving trade-offs among energy efficiency, carbon emissions, and network performance. This paper proposes NCRA-DP-ACO (Network-, Cost-, and Renewable-Aware Ant Colony Optimization with Dynamic Power Usage Effectiveness (PUE)), a bio-inspired metaheuristic that optimizes VM placement across geographically distributed datacenters. The approach integrates real-time solar energy availability, dynamic PUE modeling, and multi-criteria decision-making to enable environmentally and cost-efficient resource allocation. The experimental results show that NCRA-DP-ACO reduces power consumption by 13.7%, carbon emissions by 6.9%, and live VM migrations by 48.2% compared to state-of-the-art methods while maintaining Service Level Agreement (SLA) compliance. These results indicate the algorithm’s potential to support more environmentally and cost-efficient cloud management across dynamic infrastructure scenarios.

Suggested Citation

  • Ali Mohammad Baydoun & Ahmed Sherif Zekri, 2025. "Network-, Cost-, and Renewable-Aware Ant Colony Optimization for Energy-Efficient Virtual Machine Placement in Cloud Datacenters," Future Internet, MDPI, vol. 17(6), pages 1-30, June.
    • Handle: RePEc:gam:jftint:v:17:y:2025:i:6:p:261-:d:1678902
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1999-5903/17/6/261/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/1999-5903/17/6/261/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Shanchen Pang & Kexiang Xu & Shudong Wang & Min Wang & Shuyu Wang, 2020. "Energy-Saving Virtual Machine Placement Method for User Experience in Cloud Environment," Mathematical Problems in Engineering, Hindawi, vol. 2020, pages 1-9, May.
    2. An-ping Xiong & Chun-xiang Xu, 2014. "Energy Efficient Multiresource Allocation of Virtual Machine Based on PSO in Cloud Data Center," Mathematical Problems in Engineering, Hindawi, vol. 2014, pages 1-8, June.
    3. 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.
    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. Yeliang Qiu & Congfeng Jiang & Yumei Wang & Dongyang Ou & Youhuizi Li & Jian Wan, 2019. "Energy Aware Virtual Machine Scheduling in Data Centers," Energies, MDPI, vol. 12(4), pages 1-21, February.
    2. Stanly Jayaprakash & Manikanda Devarajan Nagarajan & Rocío Pérez de Prado & Sugumaran Subramanian & Parameshachari Bidare Divakarachari, 2021. "A Systematic Review of Energy Management Strategies for Resource Allocation in the Cloud: Clustering, Optimization and Machine Learning," Energies, MDPI, vol. 14(17), pages 1-18, August.
    3. S. H. Alsamhi & Ou Ma & Mohd. Samar Ansari & Qingliang Meng, 2019. "Greening internet of things for greener and smarter cities: a survey and future prospects," Telecommunication Systems: Modelling, Analysis, Design and Management, Springer, vol. 72(4), pages 609-632, December.

    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:jftint:v:17:y:2025:i:6:p:261-:d:1678902. 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.