A Virtual Machine Dynamic Consolidation Algorithm Based Dynamic Complementation and FFD Algorithm

Abstract

Virtual Machine Consolidation (VMC) is known as a crucial method to improve system resource utilization and service level agreement in a datacenter. In this paper, we propose a novel algorithm named Segmentation Iteration Correlation Combination (SICC) expressly for VMC. The SICC integrates the methods of statistic regression modeling, Pearson correlation coefficient analysis and off-line Bin packing and the like to establish a new process strategy to achieve excellent higher one dimensional resource utilization of datacenter. The SICC operates based on an innovative two-stage strategy. The purpose of the first stage is to reduce the difference of peak-mean value of the Virtual Machine (VM) resource utilization as much as possible by one kind of improved VM dynamic complementary consolidation algorithm, derived from the algorithm of Iterative Correlation Match Algorithm (ICMA). When the difference of peak-mean value is small enough, we can take advantage of the Bin Packing theories to improve resource utilization on account of the reasonable VM consolidation order which is inherently better than dynamic complementary consolidation algorithm. The numerical simulation indicates that the algorithm feature 3% to 20% performance improvement in resource utilization to ICMA and approximate 50% performance improvement in resource utilization to First Fit Decreasing (FFD) with the same dynamic initial conditions.