Scalable and direct vector bin-packing heuristic based on residual resource ratios for virtual machine placement in cloud data centers. (May 2018) - Jangiti, Saikishor; Sriram. V.S., Shankar
Highlights: Proposed a direct VM selection heuristic, VM nearest and available to residual resource ratios of PM. Proposed first direct VBP heuristic, multi-capacity bulk VM placement. Proposed bulk-bin-packing based VM placement, a rapid bin-packing of repeated VM requests. Both VM placements are scalable enough to place more than 10 10 VM requests. Better resource utilisation in short processing times compared to well-known vector packing heuristics. Abstract: 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.