Virtual Machine Placement in Cloud Data Centers Using Enhanced Binary Manta Ray Foraging Optimization Algorithm

Virtualization is a transformative technology that enables multiple virtual machines to operate concurrently on a single physical host, thereby rationalizing resource utilization in cloud data centers. These data centers consist of multiple servers and consume a significant amount of energy, which requires cloud providers to have mechanisms that optimize the placement of virtual machines in these servers. An optimal placement reduces energy consumption and improves resource utilization rate. In this paper, we introduce a novel algorithm based on a recent bio-inspired metaheuristic called Manta Ray Foraging Optimization (MRFO) to solve the virtual machine placement (VMP) problem. Although MRFO has been successfully applied to various engineering optimization tasks, to the best of our knowledge, it has never been used in the context of VMP. Accordingly, this paper investigates for the first time an enhanced version of the MRFO algorithm adapted for VMP problem in cloud data centers. The proposed algorithm is evaluated with CloudSim toolkit under various performance metrics, including energy consumption, resource utilization, and number of active servers, under several cloud environment heterogeneity levels. Simulation results are compared with several baseline and hybrid algorithms widely adopted in this field. The proposed algorithm achieves an improvement of 11.53% in energy consumption, 81.12% in resource utilization, and 12.68% in the number of active servers, over the best performing method.