CPU Frequency Aware Power Modeling for IoT Edge Nodes
Résumé
The Internet of Things (IoT) is used for various domains such as monitoring the environment, health care, and smart cities. Monitoring and measuring energy consumption of these systems is a crucial step in making them energy efficient.
External Hardware-based power monitoring is not always available for IoT edge nodes. An alternative is to create an accurate power model that relates easy-to-monitor parameters (e.g., instructions count, cache misses, node temperature, etc) to externally monitored power. This relationship helps to estimate the power drawn by the nodes.
IoT edge nodes have several power optimization leverages like Dynamic Voltage and Frequency Scaling (DVFS). When models calibration does not consider these leverages, the gap between power estimation and actual power usage increases.
In related works, several power models and corresponding Software-defined power meters do not consider CPU frequency on IoT edge nodes. These Software-defined power meters provide regression-based power models for IoT edge nodes. This work compares predictions made by these state of the art power models to accurate external power monitoring. We show that not considering CPU frequency can result in incorrect estimations.
We investigate and compare several methodologies for building power models, considering the CPU frequency, power, and energy leverage. Different performance metrics and regression methods are explored to estimate power usage. We demonstrate that linear and polynomial regression-based models are able to account for various CPU frequencies on IoT edge nodes. Using these models, we can predict the power consumed by IoT edge nodes running a specific workload, with a MAPE of 2% compared to accurate Hardware-based power meters.
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