A Performance Modeling of Dynamic Vehicular Clouds: Job Completion Time of Concurrently Executed Tasks
Bibliographic record
Abstract
New vehicular applications demand more computing power and real-time processing. As modern vehicles are equipped with computationally powerful but often redundant and under-utilized onboard units for autonomous driving, a network of connected vehicles can form a vehicular cloud (VC) that can provide computing services among themselves or to other devices. In this paper, we evaluate the computing performance of a VC on a highway in congested traffic. We assume that the vehicles join and leave VC at random times. Thus, the number of vehicles in the VC will be time-varying. The residency times of the vehicles in the VC will be correlated because of traffic congestion. To enable the realization of VC in the future, we need to know its computing performance that considers its dynamic nature and concurrent execution of the tasks. In this work, we determine the completion time of a job with multiple tasks with random execution times. More specifically, we derive the probability density function of the job completion time as a function of the system parameters. We provide numerical results to demonstrate the utilization of the analysis and simulation results to confirm the correctness of the analysis.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".