Power Modelling for Heterogeneous Cloud-Edge Data Centers

TL;DR

This paper presents a new method for deploying accurate, architecture-agnostic power models on heterogeneous cloud-edge data centers by selecting relevant hardware counters and developing a two-stage power prediction model.

Contribution

It introduces an automated hardware counter selection method and a two-stage power model applicable across multiple architectures, improving power prediction accuracy.

Findings

Automated counter selection matches manual methods' effectiveness.

Two-stage model outperforms classic models in dynamic power prediction.

Model works effectively on both ARM and Intel processors.

Abstract

Existing power modelling research focuses not on the method used for developing models but rather on the model itself. This paper aims to develop a method for deploying power models on emerging processors that will be used, for example, in cloud-edge data centers. Our research first develops a hardware counter selection method that appropriately selects counters most correlated to power on ARM and Intel processors. Then, we propose a two stage power model that works across multiple architectures. The key results are: (i) the automated hardware performance counter selection method achieves comparable selection to the manual selection methods reported in literature, and (ii) the two stage power model can predict dynamic power more accurately on both ARM and Intel processors when compared to classic power models.