Enhancing Resource Availability in Vehicular Fog Computing Through Smart Inter-Domain Handover
Bibliographic record
Abstract
In recent years, computer network architectures are experiencing a significant shift motivated by a myriad of edge devices generating a tremendous volume of data, service providers deploying real-time and huge bandwidth-consuming network applications, and mobile end-users demanding stringent quality of service and reduced service disruption. The fog computing architecture aims at addressing several related issues by employing computing resources at the edge of the network. However, frequent and even unexpected handover among distinct fog domains is yet a research challenge because it hinders the continuous availability of shared edge resources. In this work, we employ reinforcement learning (RL) to learn from experience how to maximize the availability of resources at fog domains by minimizing the handover frequency in vehicular scenarios through smart resource placement. We evaluated our RL-based model in simulations mimicking real-world scenarios where each moving vehicle may connect to different fog domains throughout its route. The results show that the proposed model yields an improvement in the availability of resources in comparison to a greedy strategy under all simulated scenarios.
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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 | 0.001 | 0.001 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".