Research Study Finds Energy-Efficient Virtual Machine Placement Algorithm in Cloud Datacenters

Strategic Insights -

Researchers at Beirut Arab University have proposed a new algorithm for optimizing virtual machine placement in cloud datacenters, which 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. The algorithm, called NCRA-DP-ACO, integrates real-time solar energy availability, dynamic PUE modeling, and multi-criteria decision-making to enable environmentally and cost-efficient resource allocation.

Key Takeaways:

  • The proposed algorithm, NCRA-DP-ACO, is a bio-inspired metaheuristic that optimizes virtual machine placement across geographically distributed datacenters, reducing power consumption by 13.7% and carbon emissions by 6.9%.
  • The algorithm achieves a 48.2% reduction in live VM migrations compared to state-of-the-art methods while maintaining Service Level Agreement (SLA) compliance.
  • NCRA-DP-ACO integrates real-time solar energy availability, dynamic PUE modeling, and multi-criteria decision-making to enable environmentally and cost-efficient resource allocation.
  • The algorithm is designed to support more environmentally and cost-efficient cloud management across dynamic infrastructure scenarios.
  • The research was conducted by Ali Mohammad Baydoun and Ahmed Sherif Zekri from Beirut Arab University.
  • The study's findings were published in the journal Future Internet, Volume 17, Issue 6, in 2025.

Statistics:

  • Power consumption reduction: 13.7%
  • Carbon emissions reduction: 6.9%
  • Live VM migrations reduction: 48.2%
  • Number of authors: 2
  • Institutions involved: Beirut Arab University
  • Journal title: Future Internet
  • Volume and issue number: 17(6)
  • DOI: 10.3390/fi17060261
  • Publisher: MDPI AG
  • Keywords: Beirut Arab University, Beirut, Lebanon, Asia, Climate Change, Global Warming, Future Internet, Greenhouse Gases, Machine Learning, Emerging Technologies, Information Technology, Ant Colony Optimization.

Sources:

  • NewsRx. Research Study Findings from Beirut Arab University Update Understanding of Future Internet (Network-, Cost-, and Renewable-Aware Ant Colony Optimization for Energy-Efficient Virtual Machine Placement in Cloud Datacenters). Global Warming Focus. July 7, 2025; p 927.
  • Network-, Cost-, and Renewable-Aware Ant Colony Optimization for Energy-Efficient Virtual Machine Placement in Cloud Datacenters. Future Internet, 2025,17(6):261. (Future Internet - http://www.mdpi.com/journal/futureinternet/).