Bayes Theorem Based Virtual Machine Scheduling for Optimal Energy Consumption

摘要

This paper proposes an algorithm for scheduling Virtual Machines(VM) with energy saving strategies in the physical servers of cloud data centers.Energy saving strategy along with a solution for productive resource utilizationfor VM deployment in cloud data centers is modeled by a combination of “VirtualMachine Scheduling using Bayes Theorem” algorithm (VMSBT) and VirtualMachine Migration (VMMIG) algorithm. It is shown that the overall data center’sconsumption of energy is minimized with a combination of VMSBT algorithmand Virtual Machine Migration (VMMIG) algorithm. Virtual machine migrationbetween the active physical servers in the data center is carried out at periodicalintervals as and when a physical server is identified to be under-utilized. In VMscheduling, the optimal data centers are clustered using Bayes Theorem and VMsare scheduled to appropriate data center using the selection policy that identifiesthe cluster with lesser energy consumption. Clustering using Bayes rule minimizesthe number of server choices for the selection policy. Application of Bayestheorem in clustering has enabled the proposed VMSBT algorithm to schedule thevirtual machines on to the physical server with minimal execution time. The proposedalgorithm is compared with other energy aware VM allocations algorithmsviz. “Ant-Colony” optimization-based (ACO) allocation scheme and “min-min”scheduling algorithm. The experimental simulation results prove that the proposedcombination of ‘VMSBT’ and ‘VMMIG’ algorithm outperforms othertwo strategies and is highly effective in scheduling VMs with reduced energy consumptionby utilizing the existing resources productively and by minimizing thenumber of active servers at any given point of time.