Resource Optimization based Virtual Machine Allocation Technique in Cloud Computing Domain

Cloud computing allows the clients to add or delete the dynamic resource pooling of massive capacity without adding the extra infrastructure to the client site. One of the vital challenges to delivering this feature required an efficient virtual machine allocation policy. The virtual machine allocation involves a large number of variables, and rapidly increasing the demand for resources makes it an NP-complete problem. Many heuristics and meta-heuristic-based dynamic resource allocation mechanisms are designed to generate the optimal VM allocation sequence considering the multifariousness Quality-of-Services (QoS) requirements in the cloud environment. However, most existing VM allocation policies failed to balance various Quality of Service (QoS) parameters. This work elaborated on the VM allocation problem and proposed an enhanced Genetic Algorithm (GA) to solve the optimal resource schedule to allocate requested tasks on a given number of virtual machines efficiently in cloud computing while considering various QoS parameters. By achieving the balance between the performance parameters, our developed algorithm makes ensures that the resource utilization reaches the maximum limit. Our extensive simulation experiment on the proposed algorithm is performed using the CloudSim simulation toolkit, and the comparison results prove the effectiveness against the energy and makespan parameters.