LIFE: A Predictive Approach for VM Placement in Cloud Environments

The key to maintaining high standards of quality and power conservation of physical machines in data centers lies in efficient consolidation of virtual machines (VMs). Several schemes have been proposed for this purpose; and these include online migration and VM placement - which can offer the best in terms of resource utilization. The consolidation process can be made effective by finding "opportunities" to migrate VMs as well approximating the resource utilization for the VM placement. An inefficient placement scheme, however, will lead to a substantial overloading of physical machines. This proposed VM placement scheme uses correlation coefficient and predicted future requirements of computing resources to accurately compute the value/s of variable, and has been termed LIFE - Lowest Interdependence Factor Exponent. This variable shows the extent to which VM can be associated with a target physical machine. Higher value of LIFE will correspondingly result in a larger impact factor influencing the performance of existing VMs whenever a VM is selected for migration to a target machine. To minimize performance degradation, migration of a VM to a target machine will only take place if it is found to correspond with a value of LIFE that is found to be the lowest. Intensive experiments show that the proposed scheme offers better performance attributes over Minimum Correlation Coefficient (MCC) and Power Aware Best Fit Decreasing (PABFD) schemes measured in terms of the following metrics: power consumption by 44.08% and 27.52%, SLA violation by 50.90% and 19.53% and number of VM migration by 52.91% and 9.66% respectively.