Virtual Machine (VM) placement consolidates VMs into a minimum number of Physical Machines (PMs), which can be viewed as a Vector Bin-Packing (VBP) problem. Recent literature reveals the significance of first-fit-decreasing variants in solving VBP problems, however they suffer from reduced packing efficiency and delayed packing speed. This paper presents VM NeAR (VM Nearest and Available to Residual resource ratios of PM), a novel heuristic method to address the above said challenges in VBP. Further, we have developed Bulk-Bin-Packing based VM Placement (BBPVP) and Multi-Capacity Bulk VM Placement (MCBVP) as a solution for VBP. The simulation results on real-time Amazon EC2 dataset and synthetic datasets obtained from CISH, SASTRA shows that VM NeAR based MCVBP achieves about 1.6% reduction in the number of PMs and possess a packing speed which was found to be 24 times faster than exisiting state-of-the-art VBP heuristics.

Abstract

Virtual Machine (VM) placement in a cloud data center is a Vector Bin-Packing (VBP) problem to minimize the number of PMs used for hosting the given VM requests. First-Fit-Decreasing (FFD) variants are widely used for VM placement. In this paper, a novel FFD variant, Aggregated Rank in FFD (FFD-AR) is proposed for VM placement. Simulation experiments were carried out using two datasets: a dataset inspired by Amazon EC2 instances and another is a synthetic dataset. The packing efficiency of the proposed FFD-AR results is better as compared to all the other baseline FFD variants. We believe the proposed FFD-AR can be applied to wide applications of VBP like production planning and logistics.