Reinforcement Learning-Based Edge-User Allocation with Fairness for Multisource Real-Time Systems
Bibliographic record
Abstract
The optimal choice of the edge-user allocation (EUA) has become a difficult challenge in edge computing orchestration due to the high number of edge servers, especially in the 5G era with ultra low latency providing a larger coverage for a server. We solve the EUA problem for multisource real-time systems, such as metaverse or cloud gaming, where multiple users generate data and interact with one another in real time. We use Reinforcement Learning to meet the system’s latency threshold while minimizing the variance of delay thereby achieving fairness among users. Our RL solution uses ensembles and normalization to yield efficient results: testing with a real-world cloud gaming dataset shows our solution can outperform the state-of-the-art by up to 18.7% in fairness, especially when resources become scarce. Our solution can also act as a heuristic when under-trained with carefully designed reward functions, allowing the system to quickly converge to a solution.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".