Statistical Inference of Software Performance Models for Parametric Performance Completions

Software performance engineering (SPE) enables software architects to ensure high performance standards for their applications. However, applying SPE in practice is still challenging. Most enterprise applications include a large software basis, such as middleware and legacy systems. In many cases, the software basis is the determining factor of the system's overall timing behavior, throughput, and resource utilization. To capture these influences on the overall system's performance, established performance prediction methods (model-based and analytical) rely on models that describe the performance-relevant aspects of the system under study. Creating such models requires detailed knowledge on the system's structure and behavior that, in most cases, is not available. In this paper, we abstract from the internal structure of the system under study. We focus on message-oriented middleware (MOM) and analyze the dependency between the MOM's usage and its performance. We use statistical inference to conclude these dependencies from observations. For ActiveMQ 5.3, the resulting functions predict the performance with a relative mean square error 0.1.