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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
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