Real-time scheduling algorithms proposed in the literature are often based on worst-case estimates of task parameters and the performance of an open-loop scheme can therefore be poor. To improve on such a situation, one can instead apply a closed-loop scheme, where feedback is exploited to dynamically adjust the system parameters at run-time. We propose an optimal control framework that takes advantage of feeding back information of finished tasks to solve a realtime multiprocessor scheduling problem with uncertainty in task execution times, with the objective of minimizing the total energy consumption. Specifically, we propose a linear programming-based algorithm to solve a workload partitioning problem and adopt McNaughton’s wrap around algorithm to find the task execution order. Simulation results for a PowerPC 405LP and an XScale processor illustrate that our feedback scheduling algorithm can result in an energy saving of approximately 40% compared to an open-loop method.
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