Vehicle-Assisted Service Caching for Task Offloading in Vehicular Edge Computing
Bibliographic record
Abstract
The development of artificial intelligence (AI) enables vehicular edge computing (VEC) servers to be able to provide more intelligent services. However, the limited storage resources of VEC servers constrain the deployment of intelligent service contents, which greatly restricts the intelligence level of the VEC network. To resolve this problem, we first design a novel vehicle-assisted VEC network architecture and further propose VaCo, aVehicle-assistedCollaborative caching system. VaCo allows VEC servers to download the cached service content from any vehicle in the VEC network to support task offloading. VaCo mainly considers the real-time scheduling problem of vehicle storage resources under the dynamic VEC network and the benefit problem caused by invoking vehicle resources under the highly dynamic load environment. VaCo models the vehicle storage resources as an independent resource pool and deploys a cross-VEC server content retrieval mechanism to achieve unified and efficient management of the storage resources of the vehicle cluster and the VEC server cluster. Then, we propose a multi-swarm collaborative optimization scheme to jointly optimize the service failure rate and cost, and further propose a Pareto-based optimization scheme to ensuring that VaCo can correctly evaluate the benefits of invoking vehicle resources in a dynamic VEC network. Finally, we implement VaCo and conduct extensive evaluations on real-world dataset. The experimental results on the real trajectory dataset show that VaCo can effectively utilize vehicle resources and ensure the benefits of both vehicles and VEC servers simultaneously.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 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".