Cluster-based optimal VM placement using crow search algorithm for cloud data centres

The extensive use of computational power from data centres causes huge energy consumption. A good virtual machine (VM) placement strategy would make better consolidation of VMs in a data centre (DC) that reduces energy consumption. However, it is hard to balance hosts in DCs due to the workload fluctuation by application and scaling of VMs. The optimal decision on VM placement and consolidation is an NP-hard problem and many researchers have proposed solutions to tackle this problem but they lack efficient exploitation of the mechanisms. Therefore, this paper proposes a hierarchical cluster-based approach with a meta-heuristic crow search algorithm (CSA) for the optimal selection of hosts to place the VMs and consolidate the maximum number of VMs on a minimum number of hosts. The work is simulated in CloudSim using real workload traces. Experimental results show that proposed work reduces energy consumption, SLA violations and VM migrations while ensuring better resource utilisation.