A Novel Combined Forecasting Technique for Efficient Virtual Machine Migration in Cloud Environment

Live virtual machine (VM) migration relocates running virtual machine from source physical server to the destination physical server without compromising the availability of service to the users. Live VM Migration guarantees energy saving, fault tolerance and uninterrupted server maintenance for the cloud datacenter. The workload handled by the cloud datacenters are unpredictable in nature. Hence, the migration needs intense planning. Resource starvation occurs due to dynamic nature of workload handled by cloud datacenter. The objective of this paper is to predict the resource requirement of the virtual machines running various workloads and to appropriately place them during migration. The resource requirement of the running virtual machines are predicted using combined forecast technique. The combined forecasting technique improves the forecasting accuracy. Every host machine suitably migrates based on the current and forecasted utilization. The proposed algorithm has been validated using set of simulations conducted on Google Datacenter Traces. The results show that the proposed methodology improves the forecasting accuracy.