Strategy-Proof Computational Resource Reservation Based on Dynamic Matching for Vehicular Edge Computing
Bibliographic record
Abstract
With the rapid development of autonomous driving and edge computing, vehicular edge computing (VEC) has become an emerging paradigm that allows vehicles with abundant computational resources to work as edge nodes. By introducing vehicles as infrastructures, VEC has the potential to improve users’ quality of experience and decrease operator’s deployment expenditure, especially for hot spots. In this article, a novel VEC-based resource reservation framework is designed to handle the time-varying computation requests. To articulate realistic scenarios, the online durations of provider vehicles (PVs) are assumed to be different. Besides, the PVs will not always be online to wait for the reservation assignment for the limited revenue, i.e., the PVs are dynamic and the computational resource reservation points (CRRPs) are static. In this way, dynamic matching is leveraged to model the interaction between the PVs and CRRPs. To prevent the CRRPs from manipulating their preferences for better partners, a strategy-proof and stable resource reservation algorithm is proposed to ensure all CRRPs are truthful during the resource reservation procedure. Finally, numerical simulation results are presented to validate the proofness, truthfulness, and performance of our proposed resource reservation algorithm.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.002 |
| 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".