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 allows for a decrease in the energy consumed and an increase in the resource usage 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 problem. Although MRFO was recently proposed in order to solve optimization problems for engineering applications, to the best of our knowledge, it has never been used to solve the virtual machines placement problem in the cloud. Accordingly, this paper investigates for the first time an enhanced version of the MRFO algorithm to solve the virtual machine placement problem. The proposed algorithm is evaluated with CloudSim toolkit under various performance metrics, including energy consumption, resource utilization, and number of active servers. Simulation results show that our approach outperforms five well-known metaheuristics widely adopted in this field, namely Simulated Annealing (SA), Genetic Algorithm (GA), Particle Swarm Optimization (PSO), Salp Swarm Algorithm (SSA), and Whale Optimization Algorithm (WOA).