Resource Scheduling using Enhanced Teaching– Learning-based Optimizer in Cloud Environment

Resource Scheduling (RS) in cloud environment is a significant task targeted at effective management and allocation of cloud resources in order to meet the changing user demands and applications while maximizing the system efficiency and utilization. Effective RS has gained more importance in the cloud environment for meeting the dynamic user demands, enhancing overall system performance, and optimizing resource utilization. In this research study, metaheuristic optimizing techniques have proved efficiency in tackling the RS convolution issues. This study introduces a new Resource Scheduling using Enhanced Teaching–Learning-Based Optimizer (RS-ELTBO) algorithm in Cloud Environment. The primary goal of the RS-ELTBO technique lies in the process of optimizing resource allocation and performance metrics. Applying ETLBO for resource scheduling in cloud environments can lead to improved resource utilization, reduced costs, and enhanced overall performance. The ETLBO algorithm makes use of an objective function to determine the fitness of considerable resource allocation and employs a teaching phase, learning phase, and exploration strategy to iteratively refine the solutions. The performance study of the RS-ELTBO system was tested on various aspects. The achieved values stated that the RS-ELTBO system reached improved scheduling results over other approaches.