Resource allocation in cloud data center using dynamic resource assignment frame work

Abstract

Cloud data centers experience an important testing issue in which the segment should be considered in addition to the migration of reconfigurable virtual machines. Further, one should also consider the integrated characteristics of facilitating physical machines during the relocation of VMs. In this study, a Dynamic Resource Assignment Framework (DRAM) is presented for cloud data centers. In traditional stack balance arranging calculations, only one factor is considered, for instance CPU stack in physical servers. However, the proposed strategy takes multiple factors into account such as CPU, memory and framework information for both virtual machines as well as physical machines. The authors created a joint estimate on irregularity dimensions of a cloud data center. In addition to this, every server's ordinary disproportion dimension was also considered in this study. The results inferred that DRAM is excellent in terms of execution which can be understood via its abnormality level, heavy dimension of every server and its general running time.