Assessing Predictive Models for Energy Consumption Across Varied Software Environments

This study contributes to a deeper understanding of energy consumption in software applications, emphasizing the critical need for energy efficiency. We focus on integrating performance counter events and system call data to build energy predictive models using advanced machine learning techniques, including linear regression, multi-layer perceptrons, and random forests. These models are carefully calibrated against empirical energy measurements obtained through the Perf framework. Our study addresses variability in model outcomes that stem from differences in feature selection and the inherent discrepancies of operating systems. Through various experimentation, we demonstrate that our models robustly predict energy consumption across diverse scenarios, with particularly promising results in unseen datasets. However, challenges persist in cross-application efficacy. Event-based models particularly stand out, offering reliable energy estimations in novel applications. This research validates the effectiveness of our methodologies and also illuminates the complex landscape of precise energy consumption modeling in contemporary software environments.