Energy-efficient scheduling of real-time tasks on cluster-based multicores

While much work has addressed the energy-efficient scheduling problem for uniprocessor or multiprocessor systems, little has been done for multicore systems. We study the multicore architecture with a fixed number of cores partitioned into clusters (or islands), on each of which all cores operate at a common frequency. We develop algorithms to determine a schedule for real-time tasks to minimize the energy consumption under the timing and operating frequency constraints. As technical contributions, we first show that the optimal frequencies resulting in the minimum energy consumption for each island is not dependent on the workload mapped but the number of cores and leakage power on the island, when not considering the timing constraint. Then for systems with timing constraints, we present a polynomial algorithm which derives the minimum energy consumption for a given task partition. Finally, we develop an efficient algorithm to determine the number of active islands, task partition and frequency assignment. Our simulation result shows that our approach significantly outperforms the related approaches in terms of energy saving.