Fair and efficient resource allocation optimization for internet of vehicles (IoV) in edge computing environments
Bibliographic record
Abstract
The Internet of Vehicles (IoV) rapidly develops, resulting in various computation-intensive and delay-sensitive applications. Issues of delay can be mitigated with the help of edge computing. Most studies concentrated on minimizing delays while maintaining a maximum level of task completion, either from the devices' or the requesters' perspective. This research focuses on fairness for both devices and requesters. We propose a fair resource allocation optimization model for both requesters and devices. In our model, requesters' tasks are completed relatively quickly in terms of the number of completed tasks, response time, and cost. Furthermore, by striking a balance between profits and the quantity of CPU cycles left, our suggested model ensures that devices are not overburdened. We aim to maximize the number of completed tasks while minimizing delays and preserving the fairness of requesters and devices. We perform detailed experiments on randomly generated data instances. The results in this paper show the model's effectiveness in achieving its objectives regarding various factors such as task execution time, response time, cost, and profit in IoV environments.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".