Symmetric Adaptive Population Division Based Salp Swarm Algorithm for Resource Optimization in Cloud Computing

In the present advanced world, Cloud Computing (CC) is essential across various applications because of its quick development and wide utilization of Internet technologies. However, it faces some issues in the identification of best resources and task allocation for classifying relevant tasks based on the load. The Symmetric Adaptive Population Division based Salp Swarm Algorithm (SAPD-SSA) is proposed for optimal resource allocation in the CC environment. The SAPD-SSA enhances the convergence speed by efficiently dividing populations into subgroups. This approach enhances the balance among exploration and exploitation which leads to better resource utilization in CC. Moreover, it effectively handles complex and dynamic cloud environments which ensures optimal resource allocation with less computational cost. The multi-objective functions of makespan, resource utilization and computation cost are considered in this research for estimating the effectiveness of proposed SAPD-SSA. The makespan, resource utilization and computation cost are taken as evaluation metrics for assessing SAPD-SSA performance. The SAPD-SSA attains a makespan of 250, resource utilization of 98.28% and computation cost of 950$ for the task of 200, which is superior to the Hybrid Whale optimization algorithm-with Mutation based Bees algorithm (HWOA-mBA).