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The establishment of large-scale virtualized data centers has results in the high energy consumption ,resulting in the high operating cost and carbon-di-oxide emission. Virtual machine placement is a process of mapping virtual machines to physical machines .In order to reduce the energy consumption in cloud data centers many adaptive algorithms have been designed, which uses historical data from resource usage(such as CPU ) by VMs . In the proposed work a new parameter is introduced (RAM utilization) along with CPU utilization history in threshold calculation, in order to reduce the number of VM migrations, so that the SLA violation will be reduced. The heavily loaded and little loaded hosts are migrated and the moderately loaded and lightly loaded are kept unchanged in ATEA(adaptive three-threshold energy-aware algorithm).Based on ATEA, an adaptive threshold algorithm and VM selection policies are proposed. The proposed work is tested using cloudsim toolkit. The experimental results shows that our method reduces SLA violation and energy consumption more efficiently than the available methods.

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S.Poovizhi, & N.Jayanthi. (2017). Optimized virtual machine placement algorithm for both energyawareness and SLA violation reduction in cloud data centers . International Journal of Intellectual Advancements and Research in Engineering Computations, 5(1), 658–663. Retrieved from