Energy aware virtual machine scheduling for cloud using heuristic migration

Cloud computing provides resources as services into customers by using virtualization technology. As virtual machine (VM) is hosted on physical server, great energy is consumed by maintaining the servers in cloud data center. The servers needs more energy consumption and money cost. The energy efficiency is that challenge and our method will provide a promising approach for cloud hardware and also will be able to run on a similar physical server even while having different resources. The scheduling policy helps in proper and efficient utilization of Virtual Machine’s (VMs). Both the Heuristic and the metaheuristic-based techniques have proven to have achieved some near-optimal solutions in a reasonable time frame. We analyse the current situation of cloud computing and introduce SFLA in resource allocation. To aiming that shuffled frog algorithm is easy to fall into local optimum with fast convergence speed into the subgroups of shuffled frog leaping algorithm. the number of migration and the consumption of energy than that of the previous work that is based on the particle swarm optimization (PSO) algorithm The results that shows the total simulation time (s) taken by the cloud data center when the actual number of VMs is 100 using the SFLA is less and it achieves much improved performance than the mechanism using PSO by about 18.6%.