Automated extraction of architecture-level performance models of distributed component-based systems

Abstract—Modern enterprise applications have to satisfy in-creasingly stringent Quality-of-Service requirements. To ensure that a system meets its performance requirements, the ability to predict its performance under different configurations and workloads is essential. Architecture-level performance models describe performance-relevant aspects of software architectures and execution environments allowing to evaluate different usage profiles as well as system deployment and configuration options. However, building performance models manually requires a lot of time and effort. In this paper, we present a novel automated method for the extraction of architecture-level performance models of distributed component-based systems, based on mon-itoring data collected at run-time. The method is validated in a case study with the industry-standard SPECjEnterprise2010 Enterprise Java benchmark, a representative software system executed in a realistic environment. The obtained performance predictions match the measurements on the real system within an error margin of mostly 10-20 percent. I

Automated extraction of palladio component models from running enterprise Java applications

Nowadays, software systems have to fulfill increasingly strin-gent requirements for performance and scalability. To ensure that a system meets its performance requirements during op-eration, the ability to predict its performance under different configurations and workloads is essential. Most performance analysis tools currently used in industry focus on monitoring the current system state. They provide low-level monitoring data without any performance prediction capabilities. For performance prediction, performance models are normally required. However, building predictive performance models manually requires a lot of time and effort. In this paper, we present a method for automated extraction of perfor-mance models of Java EE applications, based on monitor-ing data collected during operation. We extract instances of the Palladio Component Model (PCM)- a performance meta-model targeted at component-based systems. We eval-uate the model extraction method in the context of a case study with a real-world enterprise application. Even though the extraction requires some manual intervention, the case study demonstrates that the existing gap between low-level monitoring data and high-level performance models can be closed. 1