A Learning-Based and Network-Aware Power Management for Mobile Devices

Jin Zhang, Jiangjie Huang, Long Peng, Xiaodong Liu, Jie Yu, Wenzhu Wang

Abstract: This paper proposes a deep reinforcement learning-based power management method for mobile devices. By learning the load characteristics of the device under different usage scenarios and considering the influence of network conditions on power consumption, the CPU and GPU frequencies are dynamically adjusted for multiple application scenarios. At the same time, a “SLIDER” adjustment strategy is proposed, and combined with the system default adjustment strategy, which reduces the difficulty of adjustment and more fully utilizes the middle adjustable frequency of the CPU. The proposed method reduces power consumption by 5.3%-18% compared to state-of-the-art method.