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Virtualization technology is used to efficiently utilize the resources of a Cloud datacenter by running multiple virtual machines (VMs) on a single physical machine (PM) as if each VM is a standalone PM. Efficient placement/consolidation of VMs into PMs can reduce number of active PMs which consequently reduces resource wastage and power consumption. Therefore, VM placement algorithms need to be optimized to reduce the number of PMs required for VM Placements. In this paper, two heuristic based Vector Bin Packing algorithms called FFDmean and FFDmedian are proposed for VM placement. These algorithms use First Fit Decreasing (FFD) technique. FFD preprocesses VMs by sorting all VMs in descending order of their sizes. Since a VM is multidimensional therefore, it is difficult to decide on its size. For this, FFDmean and FFDmedian use measures of central tendency, i.e. mean and median as heuristics, respectively, in order to estimate the size of a VM. The goal of these algorithms is to utilize the PM resources efficiently so that the number of required PMs for accommodation of all VMs can be reduced. CloudSim toolkit is used to carry out the cloud simulation and experiments. Algorithms are compared over three metrics, i.e. hosts used, power consumption and resource utilization efficiency. The results reveal that FFDmean and FFDmedian remarkably outperformed two existing algorithms called Dot-Product and L2 in all three metrics when PM resources were limited.

FFD Variants for Virtual Machine Placement in Cloud Computing Data Centers