2QoSM: A Q-Learner QoS Manager for Application-Guided Power-Aware Systems

Abstract

This paper describes the design and performance of Q-learning-based quality-of-service manager (2QoSM) for compute-aware applications (CAAs) as part of platform-agnostic resource management framework. CAAs and hardware are able to share metrics of performance with the 2QoSM and the 2QoSM can attempt to reconfigure CAAs and hardware to meet performance targets. This enables many co-design benefits while allowing for policy and platform portability. The use of Q-Learning allows online generation of the power management policy without requiring details about system state or actions, and can meet different goals including error, power minimization, or a combination of both. 2QoSM, evaluated using an embedded MCSoC controlling a mobile robot, reduces power compared to the Linux on-demand governor by 38.7-42.6% and a situation-aware governor by 4.0-10.2%. An error-minimization policy obtained a reduction in path-following error of 4.6-8.9%.