Abstract
Efficient management of computing resources in cloud data centers is critical to minimize the power consumption and subsequent operating costs of the data centers. However, most of the existing approaches have several limitations during VM consolidation, including a limited number of computing resources, a higher number of VM migrations, service-level agreement (SLA) violations, and performance degradation. This paper proposes the Multiple Resource based VM Selection (MRVMS) approach for VM selection, and the Lowest Interdependence Factor Exponent Multiple Resources Predictive (LIFE-MP) approach for VM placement, by considering multiple computing resources being used simultaneously. The MRVMS approach selects a VM with high CPU requirements and optimal memory requirement for reducing the workload of overloaded PMs with minimum migration cost. The LIFE-MP approach selects a PM at which to place the migrating VM, based on the PM with the lowest correlation coefficient value among the already-running VMs and the migrating VM to reduce performance degradation because of the VM migration. Comparative results show that the proposed approaches offer better performance with respect to a power-aware best-fit decreasing (PABFD) scheme, including reducing power consumption by 29.02%, SLA violations by 32.68%, and the number of VM migrations by 66.09%.