Kelvin Ovabor and Travis Atkison, The University of Alabama, USA

In cloud computing, efficient resource allocation within data centers is crucial for reducing energy consumption and operational costs. Virtual Machine Placement (VMP) is a critical aspect, involving the strategic assignment of Virtual Machines (VMs) to physical servers. However, inefficient VM placement can lead to increased energy usage, posing significant challenges to operational efficiency and cost-effectiveness. This paper introduces a novel approach to VM placement, with the aim of minimizing total energy consumption within data centers. Leveraging the Ant Colony Optimization (ACO) algorithm, we customized its information heuristic based on the energy efficiency of physical machines (PMs) within data centers. Experimental validation demonstrates the scalability of our approach in large data center environments, where it notably outperforms the selected benchmark, the ACOVMP (Ant Colony Optimization Virtual Machine Placement) algorithm, in terms of energy consumption. Our findings highlight the effectiveness of our approach in optimizing VM placement decisions, contributing to ongoing efforts to enhance energy efficiency and operational sustainability in cloud data center environments.

Cloud, Virtual Machine, Ant Colony Optimization, Data Center, Energy Consumption