Managing Uncertainty in Autonomic Cloud Elasticity Controllers

Abstract

Elasticity allows a cloud system to maintain an optimal user experience by automatically acquiring and releasing resources. Autoscaling-adding or removing resources automatically on the fly-involves specifying threshold-based rules to implement elasticity policies. However, the elasticity rules must be specified through quantitative values, which requires cloud resource management knowledge and expertise. Furthermore, existing approaches don't explicitly deal with uncertainty in cloud-based software, where noise and unexpected events are common. The authors propose a control-theoretic approach that manages the behavior of a cloud environment as a dynamic system. They integrate a fuzzy cloud controller with an online learning mechanism, putting forward a framework that takes the human out of the dynamic adaptation loop and can cope with various sources of uncertainty in the cloud.