Virtual Machine Allocation in Cloud Computing Environments using Giant Trevally Optimizer

Cloud computing has gained prominence due to its potential for computational tasks, but the associated energy consumption and carbon emissions remain significant challenges. Allocating Virtual Machines (VMs) to Physical Machines (PMs) cloud data centers, a known NP-hard problem, offers an avenue for enhancing energy efficiency. This paper presents an energy-conscious optimization approach utilizing the Giant Trevally Optimizer (GTO) which is inspired by the hunting strategies of the giant trevally, a proficient marine predator. Our study mathematically models the trevally's hunting behavior when targeting seabirds. The trevally's approach involves strategic selection of optimal hunting locations based on food availability, including pursuing seabird prey in the air or seizing from the water's surface. Through extensive simulations, our method demonstrates superior performance in terms of skewness, CPU utilization, memory utilization, and overall resource allocation efficiency. This research offers a promising avenue for addressing the energy consumption challenges in cloud data centers while optimizing resource utilization for sustainable and cost-effective cloud operations.