Power-Aware Resource Reconfiguration Using Genetic Algorithm in Cloud Computing

Abstract

Cloud computing enables scalable computation based on virtualization technology. However, current resource reallocation solution seldom considers the stability of virtual machine (VM) placement pattern. Varied workloads of applications would lead to frequent resource reconfiguration requirements due to repeated appearance of hot nodes. In this paper, several algorithms for VM placement (multiobjective genetic algorithm (MOGA), power-aware multiobjective genetic algorithm (pMOGA), and enhanced power-aware multiobjective genetic algorithm (EpMOGA)) are presented to improve stability of VM placement pattern with less migration overhead. The energy consumption is also considered. A type-matching controller is designed to improve evolution process. Nondominated sorting genetic algorithm II (NSGAII) is used to select new generations during evolution process. Our simulation results demonstrate that these algorithms all provide resource reallocation solutions with long stabilization time of nodes. pMOGA and EpMOGA also better balance the relationship of stabilization and energy efficiency by adding number of active nodes as one of optimal objectives. Type-matching controller makes EpMOGA superior to pMOGA.