Dynamic Offloading in Mobile Edge Computing With Traffic-Aware Network Slicing and Adaptive TD3 Strategy
Why this work is in the frame
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Bibliographic record
Abstract
Network slicing and computation offloading play a pivotal role in enabling edge service providers to handle dynamic service demands effectively. However, traffic fluctuations and resource diversity pose significant challenges, often constrained by static configurations lacking flexibility. To overcome these limitations, this letter presents FlexSlice, a dynamic offloading framework designed to optimize resource allocation in mobile edge networks. Our approach leverages a sparse multi-head graph attention mechanism for precise traffic prediction, capturing complex spatio-temporal dependencies to enhance network slicing decisions. Additionally, we present an adaptive offloading strategy based on the twin delayed deep deterministic policy gradient algorithm, which incorporates twin critics and prioritized experience replay to improve decision-making under dynamic conditions. Simulation results confirm FlexSlice’s outstanding performance and adaptability in diverse operational scenarios, achieving higher profits and reliable quality of service.
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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 | 0.001 | 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 it