The problem of allocating the virtual machines to the jobs in cloud computing systems is a complex one. The key challenge inferred is attaining better power consumption, time and cost. To allocate a job, set of power-aware dynamic allocators for Virtual Machines are presented. It takes the benefit of software Defined Networking (SDN) paradigm. Integrating Ant colony algorithm augments the power consumption with its uncertain time convergence. These approaches escalated preemptive mechanism that assists better decision in scheduling. In order to overcome all those shortcomings, the Earliest Deadline First (EDF) algorithm has been implemented which tends to solve the inefficient allocation of virtual machines. The segmentation of the dataset is done to enhance the performance of the VM which is firstly realized in this VM allocation approach. In this paper, we introduce 10 virtual machines with different allocation strategies, and compare them with a baseline that consists of using the first available server (First Fit). The allocators differ in terms of allocation policy (Best Fit/Worst Fit), allocation strategy (Single/Multi objective optimization), and joint/disjoint selection of IT and network resources. The EDF algorithm is preferred here to achieve better power consumption and it is accomplished beyond the expectations. Moreover, the experimental results highlight that joint approaches outperform disjoint ones