Assessing Predictive Models for Energy Consumption Across Varied Software Environments
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".