Energy-efficient proactive edge caching with sleep scheduling for green networks
Bibliographic record
Abstract
Proactive content caching in small cells (SCs) (i.e., proactive edge caching) can significantly reduce energy consumption of networks. However, the sleep mode of SCs can make the cached contents in SCs unavailable. Therefore, a joint optimization of edge caching strategy and sleep scheduling should be conducted to maximize the effectiveness of edge caching. In this paper, we propose an energy-efficient proactive edge caching with sleep scheduling (E3CS) where the controller jointly adjusts the caching strategy and the sleep scheduling of SCs by considering the content popularity dynamics. To optimize the performance of E3CS (i.e., minimize the overall energy consumption), we formulate a Markov decision process (MDP) and the joint optimal policy on the caching strategy and sleep scheduling is obtained by a value iteration algorithm. Evaluation results demonstrate that E3CS with the optimal policy outperforms the comparison schemes in terms of the overall energy consumption.
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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 | 0.001 | 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 | 0.002 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".