10.1016/j.infsof.2020.106390
https://zenodo.org/records/4228146
oai:zenodo.org:4228146
Radu Prodan
Radu Prodan
Klagenfurt University
Nishant Saurabh
Nishant Saurabh
Klagenfurt University
A dynamic evolutionary multi-objective virtual machine placement heuristic for cloud data centers
Zenodo
2020
2020-12-01
https://zenodo.org/communities/eu
Creative Commons Attribution 4.0 International
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%.
European Commission
10.13039/501100000780
825134
smART socIal media eCOsytstem in a blockchaiN Federated environment