data center in \[23\]. Raycroft et al. \[24\] analyzed the energy consumption of the global VM allocation using various real world policies under a realistic testing scenario. However, the simulation was centered on a single application, and it does not take into consideration the communication among VMs across regions.\
Wu et al. \[25\] proposed a scheduling algorithm by using a dynamic voltage frequency scaling (DVFS) technique for the cloud data center. However, during VM consolidation and VM migration DVFS did not improve the power consumption as Beloglazov and Buyya had shown. \[18\]. An energy efficient scheduling of virtual machines (EEVS) algorithms was proposed by Ding et al. \[26\] which had reduced the energy consumption of cloud data center, however, they overlooked the VM migration and transitions of the processor. Wolke et al. \[27\] found that periodic reallocations and combinations of the controller’s placement achieved the highest energy efficiency with predefined service levels. A detail review on energy aware resource allocation of cloud data center were presented in our previous work \[32\].

VM migration and consolidation {#VM_mig_con}
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Power consumption of the cloud data center had increased because of an underutilized host and inefficiency of the resource management. On the other 