An Empirical Evaluation of Database Software Features on Energy Consumption
Why this work is in the frame
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Bibliographic record
Abstract
Although software does not consume energy by itself, its characteristics determine which hardware resources are made available and how much energy is used. Therefore, energy efficiency of software products has become a popular agenda for both industry and academia in recent years. Designing such software is now a core initiative of software development companies aiming toward social responsibility. Meanwhile, however, devel- oping environmentally sustainable software products is a challenge in that performance, functionality and energy consumption can reflect conflicting goals. In this paper, our ob- jective is to analyze the effects of different features on energy consumption of the IBM DB2, a commonly used database product. The empirical work focuses on three features. We executed a workload in preconfigured software with some features enabled or disabled and with different numbers of users. To compare the different scenarios, three sets of green metrics were utilized. The metric set identified various parts of the software system where energy is consumed. Our findings may suggest that the conflicts among software system performance, functionality, and energy consumption can be mitigated by choosing a combination of features that interact in a way that improves energy efficiency. Index Terms energy consumption, green metrics, energy efficiency, environmental sustainability, software feature interaction, database.
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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 | 0.001 | 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 | 0.000 |
| 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 it