A Queueing Model for Video Analytics Applications of Smart Cities
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Bibliographic record
Abstract
This paper aims to find a proper methodology for evaluating job scheduling strategies for a data-intensive application such as video analytics applications used for smart cities that involve edge and cloud computing. To compare two simulation methods with the analytical modeling for such evaluation, we proposed a queueing model for a system consisting of some heterogeneous edge processors and one cloud processor and compared it with a simple simulation approach. We first defined the system's characteristics and developed a queueing model for the system to calculate the edges and cloud processors' working times. We use the state-space diagram of the system to determine the set of differential equations of the system and solved them to calculate the system components' performance measures. The results show that the proposed queueing model's computational time is significantly less than other existing techniques like the simulation.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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 it