Energy-efficient resource allocation approaches with optimum virtual machine migrations in cloud environment

Recent years, Cloud Computing attracts the service provider for running applications on large data centers due to their highly available hardware, on-demand provision, and pay-as-you-go concepts. It provides the huge amount of computing power by leverages the virtualization. A virtualization technology is a promising approach to consolidating multiple Virtual machines (VM) onto a minimum number of servers. Dynamic VM provisioning, VM consolidation, and switching servers on and off as required, through all these techniques data centers can sustain the required Quality-of-Service (QoS) while accomplishing higher server utilization and energy efficiency. In our proposed work we can handle the inter-relationship between energy consumption, the number of VM migrations, SLA violation, and performance of the application. The proposed approaches handle the over-utilized servers by migrating the most appropriate VM to the suitable destination server. For this, we propose the VM selection and VM placement approaches. For overload detection, we used the exponential smoothing technique. To implement these approaches, we used cloudsim simulator. Our results show that in energy consumption can be reduced up to 42.3% and the number of VM migration is reduced as well as performances are improved in all approaches in different cases w.r.t. MAD_MU.