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Performance antipatterns document bad design patterns that have negativeinfluence on system performance. In our previous work we formalized suchantipatterns as logical predicates that predicate on four views: (i) the staticview that captures the software elements (e.g. classes, components) and thestatic relationships among them; (ii) the dynamic view that represents theinteraction (e.g. messages) that occurs between the software entities elementsto provide the system functionalities; (iii) the deployment view that describesthe hardware elements (e.g. processing nodes) and the mapping of the softwareentities onto the hardware platform; (iv) the performance view that collectsspecific performance indices. In this paper we present a lightweightinfrastructure that is able to detect performance antipatterns at runtimethrough monitoring. The proposed approach precalculates such predicates andidentifies antipatterns whose static, dynamic and deployment sub-predicates arevalidated by the current system configuration and brings at runtime theverification of performance sub-predicates. The proposed infrastructureleverages model-driven techniques to generate probes for monitoring theperformance sub-predicates and detecting antipatterns at runtime.

A model-driven approach to broaden the detection of software performance antipatterns at runtime