Cost-Aware Task Offloading and Migration for Wireless Virtual Reality Using Interactive A3C Approach
Bibliographic record
Abstract
Wireless virtual reality (VR) is becoming a promising service to provide users with immersive experience from anywhere. To deal with the performance-cost negotiation for a MEC-enabled wireless VR, we propose a cost-aware task offloading and migration scheme. We formulate the viewport rendering offloading, task migration, and subchannel allocation as an optimization problem, taking into account a long-term MEC operational cost budget and fluctuating channel conditions. To solve the problem, the Lyapunov optimization method is used to transform the long-term optimization to be a real-time optimization problem. Additionally, we design an interactive Asynchronous Advantage Actor-Critic (IA3C) algorithm to solve the problem in an online fashion. Our results show that the proposed scheme and the IA3C algorithm can substantially reduce the computing load of VR terminals while maintaining a low MEC operational cost in a high convergence rate.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| 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".