Stochastic Modeling of Auto Scaling Mechanism in Private Clouds for Supporting Performance Tuning

Elasticity mechanisms allow private cloud platforms to dynamically increase or decrease resources according to policies related to workload. Auto scaling and virtual machines (VMs) instantiation are processes which affect the performance of such mechanisms. This paper evaluates the performance of the auto scaling process on a private cloud platform considering some relevant factors which are not addressed in many related research studies. Besides the experimental study, this paper presents a Markov chain model for parametric sensitivity analysis, useful to prioritize efforts in certain process parameters. The evaluation approach and results from this study can help system administrators to properly configure the auto scaling mechanism in private clouds frameworks.