Scalable and Direct Vector Bin-Packing Heuristic Based on Residual Resource Ratios for Virtual Machine Placement in Cloud Data Centers
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) issue. Recent literature reveals the significance of first-fit decreasing variants in solving VBP problems, but they suffer from reduced packing efficiency and delayed packing speed. This Scalable and direct vector bin-packing heuristic based on residual resource ratios for virtual machine placement in cloud data centers paper presents VM NeAR (VM Nearest and Residual Resource Ratios of PM), a novel heuristic method to address the above-mentioned challenges in VBP.
Scalable and direct vector bin-packing heuristic based on residual resource ratios for virtual machine placement in cloud data centers We have also developed Bulk-Bin-Packing based VM Placement (BBPVP) and Multi-Capacity Bulk VM Placement (MCBVP) as a VBP solution. The simulation results on real-time Amazon EC2 dataset and synthetic data sets obtained from CISH, SASTRA show that VM NeAR based MCVBP achieves about 1.6 percent reduction in the number of PMs and has a packing speed that was found to be 24 times faster than existing state-of – the-art VBP heuristics.
System Configuration
Conclusion
We used AHP in this paper to find a higher price for the current services provided, and thus a means to gain more profit without having to change any infrastructure or services and at the same time without any change in rankings. The cloud environment is very dynamic, and with time the services continue to change. Customer demands continue to evolve and services change with technology development.
