Multi-Objective Optimization Using Evolutionary Algorithms
Multi-Objective Optimization Using Evolutionary Algorithms
Software Architecture in Practice
Software Architecture in Practice
Metaheuristics in combinatorial optimization: Overview and conceptual comparison
ACM Computing Surveys (CSUR)
Model-Based Performance Prediction in Software Development: A Survey
IEEE Transactions on Software Engineering
An approach for QoS-aware service composition based on genetic algorithms
GECCO '05 Proceedings of the 7th annual conference on Genetic and evolutionary computation
EMF: Eclipse Modeling Framework 2.0
EMF: Eclipse Modeling Framework 2.0
Architecture-Based Software Reliability Analysis: Overview and Limitations
IEEE Transactions on Dependable and Secure Computing
The Palladio component model for model-driven performance prediction
Journal of Systems and Software
Consideration of Partial User Preferences in Evolutionary Multiobjective Optimization
Multiobjective Optimization
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
A framework for utility-based service oriented design in SASSY
Proceedings of the first joint WOSP/SIPEW international conference on Performance engineering
Proceedings of the first joint WOSP/SIPEW international conference on Performance engineering
Rule-based automatic software performance diagnosis and improvement
Performance Evaluation
Parameterized reliability prediction for component-based software architectures
QoSA'10 Proceedings of the 6th international conference on Quality of Software Architectures: research into Practice - Reality and Gaps
A fast and elitist multiobjective genetic algorithm: NSGA-II
IEEE Transactions on Evolutionary Computation
Quality of service-oriented software systems (QUASOSS 2010)
MODELS'10 Proceedings of the 2010 international conference on Models in software engineering
Focussing multi-objective software architecture optimization using quality of service bounds
MODELS'10 Proceedings of the 2010 international conference on Models in software engineering
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Quantitative prediction of non-functional properties, such as performance, reliability, and cost, of software architectures supports systematic software engineering. Even though there usually is a rough idea on bounds for quality of service, the exact required values may be unclear and subject to tradeoffs. Designing architectures that exhibit such good tradeoff between multiple quality attributes is hard. Even with a given functional design, many degrees of freedom in the software architecture (e.g. component deployment or server configuration) span a large design space. Automated approaches search the design space with multi-objective meta-heuristics such as evolutionary algorithms. However, as quality prediction for a single architecture is computationally expensive, these approaches are time consuming. In this work, we enhance an automated improvement approach to take into account bounds for quality of service in order to focus the search on interesting regions of the objective space, while still allowing trade-offs after the search. To validate our approach, we applied it to an architecture model of a component-based business information system. We compared the search to an unbounded search by running the optimization 8 times, each investigating around 800 candidates. The approach decreases the time needed to find good solutions in the interesting regions of the objective space by more than 35% on average.