@Article{Torre2020, author = {Ennio Torre and Juan J. Durillo and Vincenzo de Maio and Prateek Agrawal and Shajulin Benedict and Nishant Saurabh and Radu Prodan}, journal = {Information and Software Technology}, title = {{A dynamic evolutionary multi-objective virtual machine placement heuristic for cloud data centers}}, year = {2020}, issn = {0950-5849}, month = {dec}, pages = {106390}, volume = {128}, abstract = {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%.}, doi = {10.1016/j.infsof.2020.106390}, keywords = {VM placement, Multi-objective optimisation, Resource overcommitment, Resource wastage, Live migration, Energy consumption, Pareto optimal set, Genetic algorithm, Data center simulation}, publisher = {Elsevier BV}, url = {https://www.sciencedirect.com/science/article/pii/S0950584919302101} }