Proceedings of the 6th international workshop on Hardware/software codesign
Real-Time Schedulability Tests for Preemptive Multitasking
WPDRTS Selected papers from the 4th workshop on Parallel and distributed real-time systems
A Fast Elitist Non-dominated Sorting Genetic Algorithm for Multi-objective Optimisation: NSGA-II
PPSN VI Proceedings of the 6th International Conference on Parallel Problem Solving from Nature
Proceedings of the conference on Design, automation and test in Europe
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ACSD '05 Proceedings of the Fifth International Conference on Application of Concurrency to System Design
An Exact Stochastic Analysis of Priority-Driven Periodic Real-Time Systems and Its Approximations
IEEE Transactions on Computers
A Method for Evaluating Uncertainties in the Early Development Phases of Embedded Real-Time Systems
RTCSA '05 Proceedings of the 11th IEEE International Conference on Embedded and Real-Time Computing Systems and Applications
Incorporating Scenarios And Heuristics To Improve Flexibility In Real-Time Embedded Systems
RTAS '06 Proceedings of the 12th IEEE Real-Time and Embedded Technology and Applications Symposium
IEEE Transactions on Very Large Scale Integration (VLSI) Systems
PISA: a platform and programming language independent interface for search algorithms
EMO'03 Proceedings of the 2nd international conference on Evolutionary multi-criterion optimization
Computing robustness of FlexRay schedules to uncertainties in design parameters
Proceedings of the Conference on Design, Automation and Test in Europe
Robust design of embedded systems
Proceedings of the Conference on Design, Automation and Test in Europe
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We are interested in mapping hard real-time applications on distributed heterogeneous architectures. An application is modeled as a set of tasks, and we consider a fixed-priority preemptive scheduling policy. We target the early design phases, when decisions have a high impact on the subsequent implementation choices. However, due to a lack of information, the early design phases are characterized by uncertainties, e.g., in the worst-case execution times (wcets), or in the functionality requirements. We model uncertainties in the wcets using the "percentile method". The uncertainties in the functionality requirements are captured using "future scenarios", which are task sets that model functionality likely to be added in the future. In this context, we derive a mapping of tasks in the application, such that the resulted implementation is both robust and flexible. Robust means that the application has a high chance of being schedulable, considering the wcet uncertainties, whereas a flexible mapping has a high chance to successfully accommodate the future scenarios. We propose a Genetic Algorithm-based approach to solve this optimization problem. Extensive experiments show the importance of taking into account the uncertainties during the early design phases.