Scheduling data flow programs in hard real-time environments

Data Flow is a natural paradigm representing distributed programs able to express data and control dependencies between composing nodes. Our work concentrates on periodic Data-Flow programs having Hard Real-Time constraints. We propose a model and a stringbased representation for a relevant class of Data-Flow programs and present a two-phase algorithm able to compute a schedule for a given set of programs to be executed. The first phase computes a feasible solution (mapping and time assignments) using a Simulated Annealing technique, the second phase takes the mapping as input and computes the optimal solution, in terms of resource allocation, using a recursive descent Quasi- Newton method.