Runtime Performance Management for Cloud Applications with Adaptive Controllers.

Adaptability is an expected property of modern software systems in order to cope with changes in the environment by self-adjusting their structure and behaviour. Robustness is a crucial component of adaptability and it refers to the ability of the systems to deal with uncertainty, i.e. perturbations or unmodelled system dynamics that can affect the quality of the adaptation. Cost is another important property to ensure that resources are used prudently and frugally, whenever possible. Engineering robust and cost-effective adaptive systems can be accomplished using a control theory approach. In this paper, we show how to implement a model identification adaptive controller (MIAC) using a combination of performance and control models and how such a system satisfies the goals for robustness and cost-effectiveness. The controller we employ is multi-input, meaning that it can issue a variety of commands to adapt the system and multi-output, meaning it can regulate multiple performance indicators simultaneously. We show that such a solution can account for uncertainty and modelling errors and efficiently adapt a web application with multiple tiers of functionality spanning multiple layers of deployment, software and virtual machines, on Amazon EC2, an actual cloud environment.