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We present a new approach to decrease task preemptions and migrations in optimal global real-time schedules on symmetric multiprocessors. Contrary to classical approaches, our method proceeds in two steps, one off-line to place jobs on intervals and one on-line to schedule them dynamically inside each interval. We propose a new linear programming formulation and a local scheduler which exhibits low complexity and produces few task preemptions and migrations. We compare our approach with other optimal scheduling algorithms, using the implicit-deadline periodic task model. Simulation results illustrate the competitiveness of our approach with respect to task preemptions and migrations.