Energy-Efficient Scheduling for Real-Time Systems Based on Deep Q-Learning Mode

被引:112
作者
Zhang, Qingchen [1 ,2 ]
Lin, Man [2 ]
Yang, Laurence T. [1 ,2 ]
Chen, Zhikui [3 ]
Li, Peng [3 ]
机构
[1] Univ Elect Sci & Technol China, Sch Elect Engn, Chengdu 611731, Peoples R China
[2] St Francis Xavier Univ, Dept Comp Sci, Antigonish, NS B2G 2W5, Canada
[3] Dalian Univ, Sch Software Technol, Dalian 116620, Peoples R China
来源
IEEE TRANSACTIONS ON SUSTAINABLE COMPUTING | 2019年 / 4卷 / 01期
关键词
Energy consumption; stacked auto-encoder; dynamic voltage and frequency scaling; Q-learning; POWER MANAGEMENT; DESIGN; ALGORITHM;
D O I
10.1109/TSUSC.2017.2743704
中图分类号
TP3 [计算技术、计算机技术];
学科分类号
0812 ;
摘要
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 4.2% energy than QL-HDS.
引用
收藏
页码:132 / 141
页数:10
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