Holistic schedulability analysis for distributed hard real-time systems
Microprocessing and Microprogramming - Parallel processing in embedded real-time systems
Frame packing in real-time communication
RTCSA '00 Proceedings of the Seventh International Conference on Real-Time Systems and Applications
Analysis and optimization of distributed real-time embedded systems
Proceedings of the 41st annual Design Automation Conference
Multi-dimensional Robustness Optimization in Heterogeneous Distributed Embedded Systems
RTAS '07 Proceedings of the 13th IEEE Real Time and Embedded Technology and Applications Symposium
An Efficient Experimental Methodology for Configuring Search-Based Design Algorithms
HASE '07 Proceedings of the 10th IEEE High Assurance Systems Engineering Symposium
Definition of Task Allocation and Priority Assignment in Hard Real-Time Distributed Systems
RTSS '07 Proceedings of the 28th IEEE International Real-Time Systems Symposium
RTSS '08 Proceedings of the 2008 Real-Time Systems Symposium
Optimizing Extensibility in Hard Real-Time Distributed Systems
RTAS '09 Proceedings of the 2009 15th IEEE Symposium on Real-Time and Embedded Technology and Applications
Stressing Search with Scenarios for Flexible Solutions to Real-Time Task Allocation Problems
IEEE Transactions on Software Engineering
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In modern embedded systems, e.g. avionics and automotive, it is not unusual for there to be between 40 and 100 processors with a great deal of the software having hard real-time requirements and constraints over how, when and where they execute. The requirements and constraints are essential to the overall systems dependability and safety (e.g. to ensure replicas execute on different hardware). This leads to a complex design space exploration (DSE) problem which cannot be practically solved manually especially if the schedule is to be maintained. In this paper it is shown that dealing with the constraints using a conventional state of the art "System Configuration Algorithm" is less efficient, less effective and does not scale well. This issue can be improved by performing constraint pre-processing as well as constraint encoding. It is shown that our approach can handle typical industrial requirements that come from the automotive industry's AUTOSAR standard in an efficient way.