Allocating hard real-time tasks: an NP-hard problem made easy
Real-Time Systems
Allocation and Scheduling of Precedence-Related Periodic Tasks
IEEE Transactions on Parallel and Distributed Systems
Scheduling Algorithms for Multiprogramming in a Hard-Real-Time Environment
Journal of the ACM (JACM)
Period-Based Load Partitioning and Assignment for Large Real-Time Applications
IEEE Transactions on Computers
Scheduling of Time-Triggered Real-Time Systems
Constraints
IEEE Transactions on Software Engineering
ICPP '97 Proceedings of the international Conference on Parallel Processing
An Open-Ended Finite Domain Constraint Solver
PLILP '97 Proceedings of the9th International Symposium on Programming Languages: Implementations, Logics, and Programs: Including a Special Trach on Declarative Programming Languages in Education
ICPP '98 Proceedings of the 1998 International Conference on Parallel Processing
CASES '02 Proceedings of the 2002 international conference on Compilers, architecture, and synthesis for embedded systems
Variable Instruction Set Architecture and Its Compiler Support
IEEE Transactions on Computers
SAFECOMP '08 Proceedings of the 27th international conference on Computer Safety, Reliability, and Security
EUC'07 Proceedings of the 2007 international conference on Embedded and ubiquitous computing
A software integration approach for designing and assessing dependable embedded systems
Journal of Systems and Software
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In this paper we consider the problem of optimal task allocation and scheduling in embedded real-time systems. This problem is far from trivial due to the wide range of complex constraints that typically appear in this type of systems. We therefore address this problem using constraint programming due to its expressive, yet powerful features. Our work includes an evaluation of different search heuristics, such as variable-value orderings and symmetry exclusion, for this particular problem domain. It is shown that by using search configurations appropriate for the problem, the average search complexity can be reduced by as much as an order of magnitude.