StratusPM: An Analytical Performance Model for Cloud Applications

Several planning and performance analysis tools and techniques are available to execute sophisticated "what-if" analysis, in order to dynamically reconfigure cloud applications, modify them, and update their adaptation policies. However, in order to utilize these tools and techniques, appropriate analytical models for such cloud software systems must first be built. Constructing such models is difficult, as it requires the knowledge of: (i) the architecture of the system (i.e., deployment model), (ii) the specifications of the platform, (iii) the behavior of the application at runtime, and (iv) the formalism and notations of the target analytical model. This is further complicated in the cloud due to: (i) the fluid nature of the cloud application models and platforms, (ii) the large number of platform providers, and (iii) the successive upgrade to the underlying platforms. In our previous research work, we developed an architectural framework and a modeling language for the cloud configuration space called StratusML. This paper aims at extending StratusML to support generating analytical performance models for cloud applications by reusing the information used to configure the platform for the deployment and the operation. We show through an example, how to use StratusPM to model cloud performance and how the new extension can help reducing the efforts needed to specify analytical performance models for cloud applications.