Towards Modeling Computation Capacity of a Vehicular Cloud While Overcoming Resource Volatility
Bibliographic record
Abstract
Future vehicles will become computationally more powerful to be safer, more autonomous, and more convenient for passengers. However, the computing resources onboard the vehicles will often be underutilized and can be pooled to form a computing cluster called vehicular cloud (VC). The VC operators inherently would like to know the capabilities of the VC to predict its performance adequately. Since the vehicles are mobile, their residency time in the VC will be random. As a result, resources in the VC will be volatile. In this work, we analyze computing capacity of a VC while overcoming its volatility characteristics. We assume that computing jobs consist of random number of tasks that can be executed independently. A job is completed when execution of all its tasks are completed. We employ a service strategy that assigns each task to a single vehicle. Further, a task is assigned to a vehicle only if the vehicle can complete its execution during the vehicle's residency time. Using a stochastic modeling approach, we provide a tractable solution to the distribution of the number of completed jobs during the lifetime of a VC, which often can not be obtained through other approaches. Then we employ the Monte Carlo simulation method to verify the numerical results from the analytical model.
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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 | 0.001 | 0.004 |
| 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 | 0.001 | 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".