Online power-aware scheduling strategy based on workload power profile measurement

Abstract

Deeply embedded systems represent large portion of devices connected in Internet of Things. Because of great number of deployed devices, power management of deeply embedded systems is an emerging aspect of their operation. The most actual power management techniques are hardware-centric and do not take run-time workload execution properties into account. We propose a novel power aware scheduling strategy for deeply embedded systems. Task-based power profiling is used to generate task power model utilized for power-aware or energy-aware task scheduling. The proposed concept dynamically changes the scheduling parameters of best-effort tasks to fit optimization goal while maintaining system hard real-time requirements. Concept is application design agnostic and can be easily integrated as a power management service available under real-time operating system support.