Modern Web applications are often hosted in a virtualized cloud computing infrastructure, and can dynamically scale in response to unpredictable changes in the workload to guarantee a given service level agreement. In this paper we propose to use Kriging surrogate models to approximate the performance profile of virtualized, multi-tier Web applications. The model is first built through a set of automated and controlled experiments at staging time, and can be later updated and refined by monitoring the Web application deployed in production. We claim that surrogate modeling makes a very good candidate for a model-driven approach to the engineering of an autonomic controller. Our experimental evaluation shows that the model predictions are faithful to the observed system's performance, they improve with an increasing amount of samples and they can be computed quickly. We also provide evidence that the model can be effectively used to synthetize an aggregated objective function, a critical component of the autonomic controller. The approach is evaluated in the context of a RESTful Web service composition case study deployed on the RESERVOIR cloud
Toffetti, G., Gambi, G., Pezze', M., Pautasso, C. (2010). Engineering Autonomic Controllers for Virtualized Web Applications. In Proceedings of the 10th International Conference on Web Engineering (pp.66-80). Springer [10.1007/978-3-642-13911-6_5].
Engineering Autonomic Controllers for Virtualized Web Applications
PEZZE', MAURO;
2010
Abstract
Modern Web applications are often hosted in a virtualized cloud computing infrastructure, and can dynamically scale in response to unpredictable changes in the workload to guarantee a given service level agreement. In this paper we propose to use Kriging surrogate models to approximate the performance profile of virtualized, multi-tier Web applications. The model is first built through a set of automated and controlled experiments at staging time, and can be later updated and refined by monitoring the Web application deployed in production. We claim that surrogate modeling makes a very good candidate for a model-driven approach to the engineering of an autonomic controller. Our experimental evaluation shows that the model predictions are faithful to the observed system's performance, they improve with an increasing amount of samples and they can be computed quickly. We also provide evidence that the model can be effectively used to synthetize an aggregated objective function, a critical component of the autonomic controller. The approach is evaluated in the context of a RESTful Web service composition case study deployed on the RESERVOIR cloud
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