A dynamic evolutionary multi-objective virtual machine placement heuristic for cloud data centers

Abstract

• Multi-objective algorithm for virtual machine (VM) placement in Cloud data centers. • Approximation of Pareto optimal set of VM placements. • Resource overcommitment, resource wastage and live migration energy tradeoff. • Island-based optimisation heuristic based on genetic NSGA-II algorithm. • Improved evaluation results compared to state-of-the-art heuristics. Minimizing the resource wastage reduces the energy cost of operating a data center, but may also lead to a considerably high resource overcommitment affecting the Quality of Service (QoS) of the running applications. The effective tradeoff between resource wastage and overcommitment is a challenging task in virtualized Clouds and depends on the allocation of virtual machines (VMs) to physical resources. We propose in this paper a multi-objective method for dynamic VM placement, which exploits live migration mechanisms to simultaneously optimize the resource wastage, overcommitment ratio and migration energy. Our optimization algorithm uses a novel evolutionary meta-heuristic based on an island population model to approximate the Pareto optimal set of VM placements with good accuracy and diversity. Simulation results using traces collected from a real Google cluster demonstrate that our method outperforms related approaches by reducing the migration energy by up to 57% with a QoS increase below 6%.