Pareto-based soft real-time task scheduling in multiprocessor systems

We develop a new method to map (i.e. allocate and schedule) real-time applications into certain multiprocessor systems. Its objectives are: the minimization of the number of processors used; and the minimization of the deadline missing time. Given a parallel program with real time constraints and a multiprocessor system, our method finds schedules of the program in the system which satisfy all the real time constraints with minimum number of processors. The minimization is carried out through a Pareto-based genetic algorithm which independently considers the both goals, because they are non-commensurable criteria. Experimental results show that our scheduling algorithm achieved better performance than previous ones. The advantage of our method is that the algorithm produces not a single solution but a family of solutions known as the Pareto-optimal set, out of which designers can select optimal solutions appropriate for their environmental conditions.