Deadline-Aware Deep-Recurrent-Q-Network Governor for Smart Energy Saving
Bibliographic record
Abstract
Complex cyber-physical-social systems (CPSS) consist of battery-supplied devices with low energy consumption requirements. It is essential to maintain the timing performance of computing or communication tasks while saving the device energy. Linux OS provides built-in frequency governors for power management. However, these governors are not able to incorporate the timing requirements of the application for decision-making and cannot adapt the power management decision to the specific application on target devices. This paper presents an intelligent Linux frequency Deep-Recurrent-Q-Network (DRQN) governor for dedicated applications with deadline requirements running on CPSS devices through machine learning. Although machine learning algorithms have made considerable breakthroughs in recent years, deploying them to real small devices is challenging because of the computational overhead. To tackle the computation overhead problem, an interactive learning framework is designed where the DRQN model performs only the lightest inference in the kernel while using the online data (time-series) to learn and update itself in real-time at the user level. The governor is tested on both standalone devices and networked devices. The experiment shows that DRQN can self-develop tradeoff policy to meet the user's need with low overhead. The energy saved by DRQN ranges from 7% to 33% for various deadlines.
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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 | 0.001 |
| 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 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".