Resource Allocation Model for Energy-Efficient Virtual Machine Placement in Data Centers

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@article{1711331,
      author = {Kabir Sholagberu Ahmed, Olushola Damilare Odejobi},
      title = {Resource Allocation Model for Energy-Efficient Virtual Machine Placement in Data Centers},
      journal = {Iconic Research And Engineering Journals},
      year = {2018},
      volume = {2},
      number = {3},
      pages = {106-124},
      issn = {2456-8880},
      url = {https://www.irejournals.com/formatedpaper/1711331.pdf},
      abstract = {The rapid expansion of cloud computing has intensified the energy demands of modern data centers, raising concerns over sustainability, operational costs, and environmental impact. Virtualization has emerged as a key enabler for improving resource utilization, yet the placement of virtual machines (VMs) across physical hosts remains a complex challenge, particularly when balancing performance guarantees with energy efficiency. Inefficient VM allocation leads to server overutilization, resource fragmentation, and increased power consumption, ultimately undermining both cost efficiency and carbon reduction efforts. This proposes a resource allocation model designed to optimize energy-efficient VM placement in large-scale data centers. The model integrates principles of virtualization, dynamic workload management, and energy-aware computing to achieve multi-objective optimization across three dimensions: resource utilization, energy efficiency, and quality of service (QoS) compliance. Core mechanisms include heuristic and AI-driven VM placement algorithms, predictive workload modeling, and dynamic migration strategies that minimize energy waste while preserving service-level agreements (SLAs). The model also incorporates thermal-aware scheduling to reduce cooling demands and policy-driven orchestration to align allocation decisions with sustainability and compliance standards. Evaluation metrics for the framework span both technical and environmental domains, including CPU, memory, and network utilization, SLA violation rates, VM migration costs, total energy consumption, and power usage effectiveness (PUE). By consolidating workloads intelligently and leveraging predictive allocation, the model reduces the number of active servers, curtails cooling requirements, and supports carbon footprint reduction without sacrificing application performance. The proposed resource allocation model offers strategic implications for cloud service providers and enterprises, enabling them to achieve cost optimization, operational resilience, and alignment with green computing objectives. Ultimately, energy-efficient VM placement represents a critical pathway toward sustainable, scalable, and environmentally responsible cloud infrastructure.},
      keywords = {Resource Allocation, Energy Efficiency, Virtual Machine Placement, Data Centers, Cloud Computing, Workload Consolidation, Power Consumption Optimization, Dynamic Resource Management, Server Utilization, Green Computing, Thermal Management, Energy-Aware Scheduling, Performance Optimization},
      month = {September},
  }