An Effective VM Consolidation Mechanism by Using the Hybridization of PSO and Cuckoo Search Algorithms

VM Consolidation is one of the prodigious challenges in Cloud Computing as VMs have to be automatically placed into a physical machine based on the load running on the corresponding physical machine i.e., host is in overloaded condition or it may be in underloaded condition. VM consolidation is enacted based on the condition i.e., either overloading or underloading of VMs into a physical host. Energy consumption in data centers is one of the huge challenges because when we consolidate the VMs into a single physical machine based on the conditions it reduces energy consumption in the data centers which is a huge advantage for the cloud provider. Many of the authors proposed VM Consolidation algorithms by addressing energy consumption as a parameter but those algorithms not meeting the standards in terms of energy consumption. In this paper, we have proposed a new hybridized Meta-heuristic approach by combining Particle swarm optimization (PSO) and Cuckoo Search (CS) algorithms for consolidation of VMs based on the status Index of VMs and thereby addressing the energy consumption as a parameter. This work is simulated on Cloudsim and the workload is generated randomly in clouds and is given as input to the algorithm. To evaluate the efficiency of the algorithm in the view of energy consumption we have compared the proposed approach against existing algorithms such as PSO and CS. Simulation results revealed that our proposed approach is improved significantly over compared algorithms with mentioned parameters.