Task Allocation for Maximizing Reliability of Distributed Computer Systems
IEEE Transactions on Computers
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RTCSA '07 Proceedings of the 13th IEEE International Conference on Embedded and Real-Time Computing Systems and Applications
An overview of evolutionary algorithms in multiobjective optimization
Evolutionary Computation
A user-centric approach for improving a distributed software system's deployment architecture
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Efficient symbolic multi-objective design space exploration
Proceedings of the 2008 Asia and South Pacific Design Automation Conference
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QoSA '08 Proceedings of the 4th International Conference on Quality of Software-Architectures: Models and Architectures
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MOMPES '09 Proceedings of the 2009 ICSE Workshop on Model-Based Methodologies for Pervasive and Embedded Software
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Electronic Notes in Theoretical Computer Science (ENTCS)
EMO'07 Proceedings of the 4th international conference on Evolutionary multi-criterion optimization
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CBSE'05 Proceedings of the 8th international conference on Component-Based Software Engineering
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REFSQ'12 Proceedings of the 18th international conference on Requirements Engineering: foundation for software quality
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Journal of Systems and Software
Optimizing decomposition of software architecture for local recovery
Software Quality Control
Entropy-based adaptive range parameter control for evolutionary algorithms
Proceedings of the 15th annual conference on Genetic and evolutionary computation
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One of the crucial aspects that influence reliability of embedded systems is the deployment of software components to hardware nodes. If the hardware architecture is designed prior to the customized software architecture, which is often the case in product-line manufacturing (e.g. in the automotive domain), the system architect needs to resolve a nontrivial task of finding a (near-)optimal deployment balancing the reliabilities of individual services implemented on the software level.In this paper, we introduce an approach to automate this task. As distinct to related approaches, which typically stay with quantification of reliability for a specific deployment, we target multi-criteria optimization and provide the architect with near-optimal (non-dominated) deployment alternatives with respect to service reliabilities. Toward this goal, we annotate the software and hardware architecture with necessary reliability-relevant attributes, design a method to quantify the quality of individual deployment alternatives, and implement the approach employing an evolutionary algorithm.