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Performance models provide the ability to predict application performance for a given set of hardware resources and are used for capacity planning and resource management. Traditional performance models assume the availability of dedicated hardware for the application. With growing application deployment on virtualized hardware, hardware resources are increasingly shared across multiple virtual machines. In this paper, we build performance models for applications in virtualized environments. We identify a key set of virtualization architecture independent parameters that influence application performance for a diverse and representative set of applications. We explore several conventional modeling techniques and evaluate their effectiveness in modeling application performance in a virtualized environment. We propose an iterative model training technique based on artificial neural networks which is found …