Qsmix: Q-learning-based task scheduling approach for mixed-critical applications on heterogeneous multi-cores

In this paper, a Q-learning-based task scheduling approach for mixed-critical application on heterogeneous multi-cores (QSMix) to optimize their main design challenges is proposed. This approach employs reinforcement learning capabilities to optimize execution time, power consumption, reliability and temperature of the heterogeneous multi-cores during task scheduling process. In QSMix, a reward function is defined to consider all target design parameters simultaneously and is tuned based on applying punishment for unwanted conditions during the learning. The learning process of QSMix is led by utilizing the defined reward function during constructing the Q-table for various execution scenarios. Afterward, the best solution is selected from the constructed Q-table based on the system’s policy to achieve a near-optimal solution that meets the existing trade-offs among objectives while considering its constraints properly. To evaluate our proposed QSMix, several experiments are performed to show its effectiveness in finding appropriate solutions and its gradual behavior during learning process. Moreover, the performance of QSMix in terms of optimizing the target design parameters is compared to various related research. The results confirm that QSMix has average improvement about 9% over related studies in joint optimization of execution time, power consumption, reliability and temperature.