Rule-based automatic software performance diagnosis and improvement
WOSP '08 Proceedings of the 7th international workshop on Software and performance
The Common Component Modeling Example
The Palladio component model for model-driven performance prediction
Journal of Systems and Software
ArcheOpterix: An extendable tool for architecture optimization of AADL models
MOMPES '09 Proceedings of the 2009 ICSE Workshop on Model-Based Methodologies for Pervasive and Embedded Software
Proceedings of the first joint WOSP/SIPEW international conference on Performance engineering
MDE-based approach for generalizing design space exploration
MODELS'10 Proceedings of the 13th international conference on Model driven engineering languages and systems: Part I
An industrial case study of performance and cost design space exploration
ICPE '12 Proceedings of the 3rd ACM/SPEC International Conference on Performance Engineering
Modeling dynamic virtualized resource landscapes
Proceedings of the 8th international ACM SIGSOFT conference on Quality of Software Architectures
Performance queries for architecture-level performance models
Proceedings of the 5th ACM/SPEC international conference on Performance engineering
Service Oriented Computing and Applications
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Designing component-based systems (CBS) that exhibit a good trade-off between multiple quality criteria is hard. Even after functional design, many remaining degrees of freedom of different types (e.g. component deployment, component selection, server configuration) in the CBS span a large, discontinuous design space. Automated approaches have been proposed to optimise CBS models, but they only consider a limited set of degrees of freedom, e.g. they only optimise the selection of components without considering the deployment, or vice versa. We propose a flexible and extensible formulation of the design space for optimising any CBS model for a number of quality properties and an arbitrary number of degrees of freedom. With this design space formulation, a generic quality optimisation framework that is independent of the used CBS metamodel can apply multi-objective metaheuristic optimisation such as evolutionary algorithms.