How to Solve Allocation Problems with Constraint Programming

In this paper1, we present a constraint programmingbased approach to solve a hard real-time allocation problem. This problem consists in assigning periodic tasks to processors in the context of fixed priority preemptive scheduling. Our approach builds on dynamic constraint programming together with a learning method to find a feasible processor allocation under constraints. This problem is decomposed into two subproblems: allocation, and schedulability. Benders decomposition is then used as a way of learning when the allocation subproblem yields a valid solution while the schedulability analysis of the allocation does not. The rationale of this approach is to learn from the failures of the schedulability analysis to reduce the search space.