Many real-time applications require dynamic scheduling with predictable performance. Tasks corresponding to these applications have deadlines to be met. Optimal scheduling of real-time tasks on multiprocessor systems is known to be computationally intractable for large task sets. In this paper, we present a hybrid genetic algorithm for nonpreemptive scheduling of dynamically arriving aperiodic real-time tasks in multiprocessor systems. The real-time tasks are characterized by their deadlines, resource requirements, and worst case computation times. The effectiveness of the proposed algorithm is shown through a simulation study.
Real-time systems, dynamic scheduling, multiprocessor systems, genetic algorithms, hybrid genetic algorithms.
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