A compositional approach to performance modelling
A compositional approach to performance modelling
Open, Closed, and Mixed Networks of Queues with Different Classes of Customers
Journal of the ACM (JACM)
Linearizer: a heuristic algorithm for queueing network models of computing systems
Communications of the ACM
Some Extensions to Multiclass Queueing Network Analysis
Proceedings of the Third International Symposium on Modelling and Performance Evaluation of Computer Systems: Performance of Computer Systems
Model-Based Performance Prediction in Software Development: A Survey
IEEE Transactions on Software Engineering
Performance by unified model analysis (PUMA)
Proceedings of the 5th international workshop on Software and performance
Model-Based performance prediction with the palladio component model
WOSP '07 Proceedings of the 6th international workshop on Software and performance
A Model Transformation from the Palladio Component Model to Layered Queueing Networks
SIPEW '08 Proceedings of the SPEC international workshop on Performance Evaluation: Metrics, Models and Benchmarks
MDD4SOA: Model-Driven Service Orchestration
EDOC '08 Proceedings of the 2008 12th International IEEE Enterprise Distributed Object Computing Conference
Enhanced Modeling and Solution of Layered Queueing Networks
IEEE Transactions on Software Engineering
Non-functional properties in the model-driven development of service-oriented systems
Software and Systems Modeling (SoSyM)
Web Application Performance Modeling Using Layered Queueing Networks
Electronic Notes in Theoretical Computer Science (ENTCS)
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We present a method for the prediction of the performance of a service-oriented architecture during its early stage of development. The system under scrutiny is modelled with the UML and two profiles: UML4SOA for specifying the functional behaviour, and MARTE for the non-functional performance-related characterisation. By means of a case study, we show how such a model can be interpreted as a layered queueing network. This target technique has the advantage to employ as constituent blocks entities, such as threads and processors, which arise very frequently in real deployment scenarios. Furthermore, the analytical methods for the solution of the performance model scale very well with increasing problem sizes, making it possible to efficiently evaluate the behaviour of large-scale systems.