Modelling of horse herd optimization based multi objective task scheduling approach in cloud computing environment

Cloud computing, which offers scalable and flexible resources, faces a key challenge in task scheduling, directly impacting system performance and user satisfaction. Effective scheduling is crucial for optimizing resource use and reducing makespan. The NP-completeness of the task scheduling problem complicates achieving optimal outcomes. Scheduling applications is critical in cloud computing due to the need to map future tasks to resources in real time. Many existing methods focus on makespan and resource consumption but overlook factors like energy usage and migration time, which affect web services. This study proposes a horse herd optimization-based multi-objective task scheduling approach (HHO-MOTSA) to address these gaps. The HHO-MOTSA aims to minimize makespan, energy usage, and cost by modelling the social behaviors of horses, including grazing, hierarchy, sociability, and defense mechanisms. A fitness function helps evaluate solutions, where a low value indicates minimized energy, makespan, and cost. Performance tests using CloudSim show that HHO-MOTSA outperforms other methods in effec?tive task scheduling.