Task Migrations in a Cloud Computing Virtual Machine Using Cauchy Swarm Optimization Techniques

By offering virtual resources to end users, cloud computing model is significant to address the difficulty of management of memory. To determine the cloud workloads and subsequently apply the migration algorithm for efficient resource use, several techniques were applied. The procedure is reliant on previous results and just a few steps ahead projections. As a result, the allocation takes longer to process and an essential element allocation of resources is provided in order to allocate and use resources efficiently in response to user requests. Virtualization aims to reduce the expense of running programmes on separate hardware sets. The attribute selection is done using Independent Component Analysis (ICA) and relief. The hybrid Cauchy particle swarm technique is then used to allocate resources based on the selected attribute. The Cluster dataset is used to assess the proposed technique's migrant count and power consumption. The optimal strategic outperforms both the present methodology and the existing method by reducing the energy consumption and the frequency of virtual machine migrations. As a result, for dynamically assigning cloud resources, the proposed ICA and relief-based MCPSO are the best options.