Energy Efficient Scheduling for Real Time Systems Based on Deep Q Learning Model
Energy saving is a critical and challenging issue for real time systems in embedded devices because of their limited energy supply. To reduce the energy consumption, a hybrid dynamic voltage and frequency scaling (DVFS) scheduling based on Q learning (QL HDS) was proposed by combining energy efficient DVFS techniques. However, QL HDS discretizes the system state parameters with a certain step size, resulting in a poor distinction of the system states. More importantly, it is difficult for QL HDS to learn a system for various task sets with a Q table and limited training sets. In this paper, an energy efficient scheduling scheme based on deep Q learning model is proposed for periodic tasks in real time systems (DQL EES) Specially, a deep Q learning model is designed by combining a stacked auto encoder and a Q learning model. In the deep Q learning model, the stacked auto encoder is used to replace the Q function for learning the Q value of each DVFS technology for any system state. Furthermore, a training strategy is devised to learn the parameters of the deep Q learning model based on the experience replay scheme. Finally, the performance of the proposed scheme is evaluated by comparison with QL HDS on different simulation task sets. Results demonstrated that the proposed algorithm can save average <inline formula><tex math notation="LaTeX"$4.2\/tex math><alternatives><inline graphic xlink:href="yang ieq1 2743704.gif"/alternatives>/inline formula> energy than QL HDS.
IEEE Transactions on Sustainable Computing