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A dynamic evolutionary multi-objective virtual machine placement heuristic for cloud data centers
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DOI：10.1016/j.infsof.2020.106390.png)
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Abstract
En 中文
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%.
Keywords:
VM placement
Multi-objective optimisation
Resource overcommitment
Resource wastage
Live migration
Energy consumption
Pareto optimal set
Genetic algorithm
Data center simulation
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