An Empirical Evaluation of Database Software Features on Energy Consumption
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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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.035 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 source (direct Gemma or distilled Codex), 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".