Effective resource management through VM allocation in cloud data center

It is essential to allocate virtual machines efficiently in order to optimize the resource utilization and minimize the total energy consumption in data centers (DCs). Thus, reducing the number of operational physical machines will reduce the overall energy consumption of the cloud DC. In this paper, First Fit (FF), First Fit Decrease (FFD), Best Fit (BF), Best Fit Decrease (BFD) and Genetic Algorithm (GA) heuristics have been used to allocate virtual machines (VMs) to physical machines (PMs) to get the optimal mapping. Furthermore, we estimated the total energy consumption by the resource requirements of PMs that are required to process the assigned VMs. The experimental results indicate that optimizing the number of active PMs is not always enough to minimize the total energy consumption of the DC. GA is able to get the optimal number of active PMs, but it does not always reduce the total energy consumption of the DC compared to the other employed algorithms. This highlights the necessity of considering energy consumption as a separate objective during virtual machine allocation and consolidation planning as opposed to minimizing the number of active PMs to reduce the power consumption of the data center.