Intelligent and Decentralized Resource Allocation in Vehicular Edge Computing Networks
Bibliographic record
Abstract
With the rise of intelligent transportation systems and the increasing diversity of vehicular applications, such as safety-related features, parking navigation, and multimedia applications, vehicular edge computing has garnered significant attention. However, managing task offloading efficiently to meet the demands of various tasks remains a fundamental research challenge due to the workload dynamics at multi-access edge computing (MEC) and the unpredictable arrival of tasks. To tackle these challenges, this work proposes a task offloading algorithm for a dynamic vehicular network based on task priority. We introduce a new resource allocation problem to ensure critical tasks meet their response time requirements. The algorithm utilizes Multivariate Long Short-Term Memory (LSTM) to develop an intelligent workload prediction for each MEC node. Additionally, we employ distributed deep reinforcement learning to enhance the efficiency and accuracy of the proactive resource allocation algorithm. Extensive numerical analysis and results demonstrate that our proposed algorithm can significantly increase the ratio of accepted critical tasks. Overall, our task offloading algorithm can effectively manage resources and meet the demands of various tasks in a dynamic vehicular network.
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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) | 0.000 | 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 | 0.001 |
| Research integrity | 0.000 | 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".