Towards Automated System-Level Energy-Efficiency Optimisation using Machine Learning
Benedict Herzog, Stefan C. Reif, Fabian Hügel, Timo Hönig, Wolfgang Schröder‐Preikschat · 2021
Modern computing systems need to execute applications in an energy-efficient manner. To this end, operating systems, middleware, and run-time systems offer plenty of parameters that support fine-tuning their behaviour. However, their individual and combined impact on performance and power draw is so complex that this optimisation potential is often ignored in practice. This paper therefore discusses a cross-layer system design that uses machine learning internally to enable fine-tuning run-time systems to their current workload. Our approach includes all layers, from the hardware to the application, considering both performance and power draw.