Optimizing Tasks Assignment on Heterogeneous Multi-core Real-Time Systems with Minimum Energy
The main challenge for embedded real-time systems, especially for mobile devices, is the trade-off between system performance and energy efficiency. Through studying the relationship between energy consumption, execution time and completion probability of tasks on heterogeneous multi-core architectures, we propose an Accelerated Search algorithm based on dynamic programming to obtain a combination of various task schemes which can be completed in a given time with a confidence probability by consuming the minimum possible energy. We adopt a DAG (Directed Acyclic Graph) to represent the precedent relation between tasks and develop a Minimum-Energy Model to find the optimal tasks assignment. The heterogeneous multi-core architectures can execute tasks under different voltage level with DVFS which leads to different execution time and different consumption energy. The experimental results demonstrate our approach outperforms state-of-the-art algorithms in this field (maximum improvement of 24.6%).