QoS-Aware Power Management via Scheduling and Governing Co-Optimization on Mobile Devices

Abstract

Scheduling and governing are two key technologies to trade off the Quality of Service (QoS) against the power consumption on mobile devices with heterogeneous cores. However, there are still defects in the use of them, among which two of the decoupling issues are critical and need to be resolved. First, both the scheduling and governing decouple from QoS, one of the most important metrics of user experience on mobile platforms. Second, scheduling and governing also decouple from each other in mobile systems and they might weaken each other when being effective at the same time. To address the above issues, we propose Orthrus, a comprehensive QoS-aware power management approach that involves a governing approach based on deep reinforcement learning to adjust the frequency of heterogeneous cores, a scheduling algorithm based on finite state machine that assigns cores to QoS-related threads, and expert fuzzy control-based coordination mechanism between the two to manage the impact between scheduling and governing. Our proposed approach aims to minimize power consumption while guaranteeing the QoS. We implement Orthrus on Google Pixel 3 as the system service of Android and evaluate it using several widespread mobile applications. The performance evaluation demonstrates that Orthrus reduces the average power consumption by up to 35.7% compared to three state-of-the-art techniques while ensuring the QoS on mobile platforms.

QianLong Sang

Thrid Year CS Phd

My research interests include operating system, computer architecture and computer security.