Optimal autoscaling in a IaaS cloud

Authors:
Hamoun Ghanbari;Bradley Simmons;Marin Litoiu;Cornel Barna;Gabriel Iszlai
Affiliations:
York University, Toronto, ON, Canada;York University, Toronto, ON, Canada;York University, Toronto, ON, Canada;York University, Toronto, ON, Canada;IBM Toronto Lab, Toronto, ON, Canada
Venue:
Proceedings of the 9th international conference on Autonomic computing
Year:
2012

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Abstract

An application provider leases resources (i.e., virtual machine instances) of variable configurations from a IaaS provider over some lease duration (typically one hour). The application provider (i.e., consumer) would like to minimize their cost while meeting all service level obligations (SLOs). The mechanism of adding and removing resources at runtime is referred to as autoscaling. The process of autoscaling is automated through the use of a management component referred to as an autoscaler. This paper introduces a novel autoscaling approach in which both cloud and application dynamics are modeled in the context of a stochastic, model predictive control problem. The approach exploits trade-off between satisfying performance related objectives for the consumer's application while minimizing their cost. Simulation results are presented demonstrating the efficacy of this new approach.