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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 …