Autonomous Power Management With Double-<i>Q</i>Reinforcement Learning Method
Bibliographic record
Abstract
Energy efficiency and autonomous power management are extremely important for mobile-edge computing. Reducing energy consumption of a number of applications running concurrently in mobile devices while maintaining performance poses a challenge to energy optimization due to the limited capacity of the embedded battery. To extend battery life and offer a long-lasting working energy, dynamic voltage and frequency scaling (DVFS) has been widely used in mobile devices for energy consumption minimization. However, most conventional DVFS techniques scale operating frequency based on static policies, and thus, they are difficult to be adapted to systems of varied conditions. In order to improve adaptivity, in this article, we proposed a Double-Q power management approach to scale operating frequency based on learning. The Double-Q method stores two Q tables and two corresponding update functions. In each decision point, either of Q tables is randomly chosen and updated, while the other is used for the measurement. This mechanism reduces the overestimation in Q values, consequently enhancing the accurateness of frequency predictions. To evaluate the effectiveness of our proposed approach, a Double-Q governor is implemented in the Linux kernel. Our approach is computationally light, and experimental results indicate that it achieves at least 5-18% total energy saving compared to on-demand and conservative governors, as well as Q learning-based method.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".