Scheduling the Task of User in Cloud computing using Hybrid Procedure of PSO and Lion Algorithm

Commonly used for tasks that may be performed on a network of remote servers or “processing units,” cloud computing is a popular platform for executing operations on virtual computers. In both the consumer and the provider realms, task scheduling is an important tool. Finding the best resource to complete a given work is a major challenge for any task scheduler. Thus, the researchers saw job scheduling as one of cloud computing's most basic challenges. Several methods have been proposed by academics to handle the optimization issue that is the optimum determination of scheduling criteria in cloud computing. This work presents a novel hybrid approach that combines the concepts of the Lion Algorithm with the Particle Swarm Optimization-(PSO) algorithm to address the aforementioned optimization problem. The research used both traditional parallel workloads and artificial workloads to perform its tests using the CloudSim toolset. The findings are compared to those produced by other similarly-structured, metaheuristic-based methods tested under the identical circumstances. The promising findings of our simulations suggest that our suggested method has real promise in this area. To reduce the time it takes to submit a particular process in a virtual environment, this study proposes improved versions of the PSO and LA algorithm, subject to user-required budgetary limitations. The results also indicate that, when applied to workflow issues operating on different virtual machines, improved versions of the PSO and LA algorithm outperform the original technique in terms of shorter schedule duration.