Task Scheduling in Cloud Computing Using a Hybrid Bobcat Optimization Algorithm with Humboldt Squid Optimization

Task scheduling is the most significant resource optimization and efficient system performance method employed in cloud computing. This paper presents the Hybrid Bobcat Optimization Algorithm (BSHO), a novel method that hybridizes the Bobcat Optimization Algorithm (BOA) and Humboldt Squid Optimization (HSO) to reduce the complexity of task scheduling in a dynamic cloud environment. The goal of the suggested framework is to maximize task allocation to virtual machines (VMs), minimize makespan, optimize the usage of resources, and minimize energy consumption. BSHO leverages the local search feature of BOA and the global search techniques of HSO and thus facilitates adaptability to diverse cloud environments and workloads. The performance of BSHO is evaluated using significant parameters such as execution cost, task completion rate, CPU utilization, and fitness value. Experiment outcomes indicate that BSHO is better than classic scheduling algorithms in that it achieves a lower cost of execution ranging from $800 to $1300, while reducing the task completion time to between 20 ms and 45[Formula: see text]ms based on the VM number. In addition, BSHO ensures a maximum load balancing effectiveness of up to 89% and a rate of task execution of 98%, thus ranking as a highly effective scheduling algorithm in cloud computing systems. The outcomes warrant that BSHO presents a solid, efficient, and scalable solution for improving cloud task scheduling.