In this paper, we propose novel low-energy static and dynamic scheduling algorithms with low computational complexities, for heterogeneous multiprocessor real-time embedded systems. We consider task graphs with deadlines and precedence relationships to satisfy. We propose a novel scheme, referred to as "critical-path information track-and update", based on critical-path analysis to distribute the slack-time over tasks such that energy consumption is minimized, while guaranteeing the precedence and timing constraints. Our dynamic scheduling algorithm applies the static scheduling algorithm during runtime based on the updated average-case execution demands of tasks. Our simulation results show that the proposed static scheduling algorithm consumes only 2% of the computational time with no degradation in energy savings, whereas the dynamic scheduling algorithm delivers up to 25% more energy savings while reducing the computational time overhead by more than 90%, when compared with recent heterogeneous multiprocessor scheduling algorithms.
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