Gaussian Based Convergence Factor with Squirrel Search Algorithm for Optimal Resource Allocation in Cloud Computing for Small Finance Organization

Cloud Computing (CC) have high attention because of executing the requests, accordance with user needs and gives quality services and task execution time among Virtual Machines (VM). But, the resource allocation for different applications is the problem because of dynamic workload conditions and uncertainty in cloud network. In this manuscript, the Gaussian based Convergence Factor (GCF) with Squirrel Search Algorithm (SSA) for allocating the optimal resource in cloud environment for small finance organization. The proposed GCF with SSA effectively balances the workload and allocates much appropriate resources to users’ application when ensuring a deadline constraint. The Tent chaotic map and Gaussian based Convergence Factor (GCF) are incorporated in the tradition SSA which improved the search ability and convergence rate of SSA to allocate the optimal resources in cloud for small finance organization. By selecting the correct instance types which aligns with requirements of finance organization and this involves deciding between options like on-demand, reserved or spot instances. The performance of GCF with SSA is evaluated with different metrics of implementation time, makespan, vigour ingesting and reserve utilization. The GCF with SSA reached less energy of 0.505J, less execution time of 0.472s, less makespan of 0.723s and high resource utilization of 51% for 100 tasks of 30 VMs which is efficient while compared to existing methods like Moth Search Adapted Sealion Optimization (MS-SLnO). DOI:https://doi.org/10.52783/pst.1664