Increased growth of utility based services in the industrial sides leads to large scale cloud data centers construction. Increased growth of cloud services leads to more energy consumption which can be managed optimally by introducing the virtual machine consolidation procedure, so that resource sharing can be performed. In the existing work, Dynamic VM consolidation (DVMC) technique is introduced for the optimal management of resources with optimal energy consumption. However in this work accurate and optimal decision making is not guaranteed. This is focused and resolved in proposed research work using introduction of Machine Learning based Optimal VM Consolidation Decision Making Method (ML-OVM-CDM). In this research work, initially over utilized and underutilized VM present in the data centers would be predicted. After prediction, virtual machine to be migrated will be selected from the available data centers. Finally the data host in which selected VM will be migrated would be chosen for the migration outcome. This decision making about the VM migration would be taken by using modified SVM algorithm. In java simulation environment, proposed research method is evaluated and it shows that proposed method is having better performance than existing work.
Volume 12 | 04-Special Issue
Pages: 501-508
DOI: 10.5373/JARDCS/V12SP4/20201515