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Real-Time systems must ofen handle several independent periodic macro-tasks, each one represented by a general tasks graph, including communications and precedence constraints. The implementation of such applications on a distributed system communicating via a bus , requires tasks assignment and scheduling, as well as the taking into account of the communication delays. As periodicity implies macro-tasks deadlines, the problem ofjinding afeasible schedule is critical. This paper addresses this NP-hard problem resolution, by using a genetic algorithm, under o fline and non-preemptive scheduling assumptions. This algorithm performances are evaluated on a large simulation set, and compared to classical list-based algorithms, a simulated annealing algorithm and a specijic clustering algorithm.

In this paper, we present an original approach (CPRTA for "Constraint Programming for solving Real-Time Allocation") based on constraint programming to solve a static allocation problem of hard real-time tasks. This problem consists in assigning periodic tasks to distributed processors in the context of fixed priority preemptive scheduling. CPRTA is built on dynamic constraint programming together with a learning method to find a feasible processor allocation under constraints. Two efficient new approaches are proposed and validated with experimental results. Moreover, CPRTA exhibits very interesting properties. It is complete (if a problem has no solution, the algorithm is able to prove it); it is non-parametric (it does not require specific tuning) thus allowing a large diversity of models to be easily considered. Finally, thanks to its capacity to explain failures, it offers attractive perspectives for guiding the architectural design process.