An Efficient Wolf Optimizer System for Virtual Machine Placement in Wireless Network Over the Cloud Environment

The drastic growth in cloud utilization makes the investigator faces a crucial issue with the virtual machine (VM) placement. Generally, VMs are allocated to physical servers for provisioning on-demand requests. This process is relatively straightforward but leads to performance degradation based on resource availability. Also, the optimal VM placement leads to unnecessary migration and network traffic. Thus, cloud storage system efficiency relies on the placement of the VM and the service provided by it. Various investigators attempt to provide an efficient placement strategy which improves QoS and reduces the computation cost. This research work concentrates on handling the VM placement problem and reducing data centres' power consumption and resource utilization. Here, a multi-objective constraint is formulated and resolved using bio-inspired optimization approach. An improved Grey-wolf optimization (IGWO) approach is proposed to leverage the multi-level optimal VM placement to provide an optimal solution concerning exploration and exploitation. The theoretical model depicts the proposed (IGWO) efficiency, which substantially enhances the system performance. The proposed IGWO model gives better results than the prevailing approaches. The extensive simulation process is carried out in a MATLAB environment.