Parked Vehicles Task Offloading in Edge Computing
Bibliographic record
Abstract
The analytical research has recently indicated that the computational resources of Connected Autonomous Vehicles (CAVs) have been wasted since almost all vehicles spend over 95% of their time in parking lots. This paper presents a collaborative computing framework to efficiently offload online computational tasks to parked vehicles (PVs) during peak business hours. To maintain the service continuity, we advocate for integrating Kubernetes-based container orchestration to leverage its advanced features (e.g., auto-healing, load balancing, and security). We analytically formulate the task-offloading problem and then propose an intelligent meta-heuristic algorithm to dynamically deal with online heterogeneous demands. Additionally, we take a cumulative incentives model into account, where the PV owners are able to earn profit by sharing their computation resources. We also compare our algorithm with several existent heuristics on different sizes of the parking lot. Extensive simulation results show that our proposed computing framework significantly increases the possibility of accepting the online tasks and improves average task offloading cost by at least 40%. Besides, we quantify the PV availability by task acceptance ratios, which can be a critical criterion for network planners to achieve desired network service goals.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".