Adaptive Edge Caching in Dynamic Environments Using PPO and Transfer Learning
Bibliographic record
Abstract
This study tackles the problem of edge caching within dynamic settings, where increasing traffic demands put pressure on backhaul links and core network infrastructures. We introduce a caching method based on Proximal Policy Optimization (PPO) that integrates essential file characteristics, including size, lifetime, importance, and popularity, while also accommodating random file request patterns to better mirror real-world edge caching situations. Dynamic environments often experience fluctuations in content popularity and request rates, rendering previously established policies less effective since they were tailored to earlier conditions. Although training a new policy from scratch in a changed environment is feasible, it is often inefficient and resource-intensive. To solve this issue, we present a PPO algorithm enhanced with transfer learning, which improves convergence in new environments by utilizing previously acquired knowledge. Our simulation results highlight the substantial advantages of our approach, outperforming recent transfer learning-based methods in terms of convergence rate, demonstrating its potential to enhance edge caching in dynamic real-world scenarios.
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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 | 0.000 |
| 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 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".