Optimizing Cloud Computing Energy Efficiency with a Grasshopper-Inspired Technique for Virtual Machine Migration

This research paper presents a novel approach using the grasshopper algorithm to improve the energy efficiency of virtual machine (VM) migration in cloud computing. This methodology reframes resource allocation techniques to address the complexity of VM selection from heavily utilized physical machines (PMs). Its effectiveness is evaluated in comparison to traditional VM migration techniques and optimization algorithms such as Firefly and Artificial Bee Colony (ABC). Significant metrics that act as evaluation criteria for algorithm include the reduction of energy usage, mitigation of service level agreement violations (SLA-V), and overall resource optimization. The research paper not only presents a novel approach but also provides significant perspectives for the advancement of dynamic resource allocation in cloud systems in the future. It creates opportunities for enhancing energy efficiency in the constantly changing field of cloud computing by exploring computational approaches inspired by nature.