Cloud computing technology helps to resolve the problem in storage management by providing virtual resources to the end users. But, the overloading of virtual machines results in degradation of performances as well as it increases in the energy consumption of the virtual machines. Several techniques were used to determine the workloads of the cloud and then apply the migration algorithm for efficient utilization of resources. But, the process depends on the past outputs and only few step ahead predictions. Most of the techniques allocate the resources based on all the attributes. This results in higher processing time for the allocation. Hence, in this, an attribute based resource allocation is proposed to allocate and utilize the resources effectively based on the user demands. The modified Principal component analysis and relief is used for the attribute selection. Then, the selected attribute is processed with the hybrid Cauchy particle swarm algorithm for the allocation of resources. The proposed method is tested google cluster dataset and its performance is evaluated in terms of migration count and power consumption. The proposed method performance is compared with the automated migration technique (ALM) and forecast based migration technique (CF-LA). The proposed method outperforms both the existing technique by reducing the power consumption and the migration count between the virtual machines. Hence, the proposed MPCA and relief basedCPSO is best for allocating the resources dynamically in the cloud.