Enhanced Cloud Resource Optimization Using Secretary Bird Optimization Algorithm and Dynamic Workload Balancing

In this paper, a framework for optimizing cloud resource allocation using the Secretary Bird Optimization Algorithm and dynamic workload balancing has been presented. The presented method tackles inefficiency in cloud computing systems, such as inefficiency in task scheduling and resource overload, through dynamic task distribution among resources. With the introduction of SBOA, prompted by the innate behavior of secretary birds, and workload balancing, the approach achieves maximum resource utilization, minimum makespan, and lower computational expense. Global attention mechanisms are further incorporated into the framework to direct computational effort at the most salient features, which improves efficiency. Simulation results show improved cloud resource optimization by a significant extent over conventional approaches, rendering the method feasible for dynamic, large-scale cloud environments.