A Scalable Hybrid Optimized Firefly Optimization with Simulated Annealing for Scheduling Tasks in Cloud Environments
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Abstract
Task scheduling is crucial to the performance of the whole cloud computing infrastructure and is thus the most essential need in a cloud computing environment. The process of allocating the most appropriate resources for work while taking various factors into account is known as task scheduling in cloud computing. There is a discrete optimization problem called NP-hard that describes the task scheduling problem. Hybrid swarm optimization is the proposed solution to this issue. Hybrid Optimized Firefly Optimization with Simulated Annealing (HOFO-SA) is a new hybrid optimization methodology for cloud computing job scheduling that is scalable. By integrating the exploration capabilities of firefly swarm optimization with the exploitation strengths of simulated annealing, the proposed approach efficiently balances the computational load across virtual machines. The suggested technique was tested through simulations run on the CloudSim simulator. Experimental evaluations were conducted under both dynamic and static load balancing scenarios in terms of performance metrics, including Makespan, Execution Time, Completion Time, Variance, Total Execution Cost, Average and Resource Utilization on 5 to 25 VMs and 100 to 600 number of tasks. The experimental results demonstrate the effectiveness of HOFO-SA, showing a 32.5% reduction in makespan, a 28.7% improvement in execution time, and a 36.8% increase in resource utilization efficiency ratio showed substantial improvements, reaching 0.99 in HOFO-SA compared to 0.84 in compare Firefly Swarm Optimization Task scheduling (FSOTS) traditional approach. These improvements lead to better workload distribution, enhanced system reliability, and reduced processing delays, making HOFO-SA a promising solution for cloud resource scheduling. This work highlights the significance of hybrid optimization techniques in addressing complex scheduling challenges and improving overall cloud computing performance.
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