Cloud Data centers have adopted virtualization techniques for effectiveand efficient compilation of an application. The requirements of application from theexecution perspective are fulfilled by scaling up and down the Virtual Machines(VMs). The appropriate selection of VMs to handle the unpredictable peak workloadwithout load imbalance is a critical challenge for a cloud data center. In this article,we propose Pareto based Greedy-Non dominated Sorting Genetic Algorithm-II(G-NSGA2) for agile selection of a virtual machine. Our strategy generates Paretooptimal solutions for fair distribution of cloud workloads among the set of virtualmachines. True Pareto fronts generate approximate optimal trade off solution formultiple conflicting objectives rather than aggregating all objectives to obtain singletrade off solution. The objectives of our study are to minimize the response time,operational cost and energy consumption of the virtual machine. The simulationresults evaluate that our hybrid NSGA-II outperforms as compared to the standardNSGA-II Multiobjective optimization problem.
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