A Cost-Benefit Model for Feasible IoT Edge Resources Scalability to Improve Real-Time Processing Performance
Why this work is in the frame
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Bibliographic record
Abstract
Abstract Edge computing systems have emerged to facilitate real-time processing for delay-sensitive tasks in Internet of Things (IoT) Systems. As the volume of generated data and the real-time tasks increase, more pressure on edge servers is created. This eventually reduces the ability of edge servers to meet the processing deadlines for such delay-sensitive tasks, degrading users’ satisfaction and revenues. At some point, scaling up the edge servers’ processing resources might be needed to maintain user satisfaction. However, enterprises need to know if the cost of that scalability will be feasible in generating the required return on the investment and reducing the forgone revenues. This paper introduces a cost-benefit model that values the cost of edge processing resources scalability and the benefit of maintaining user satisfaction. We simulated our cost-benefit model to show its ability to decide whether the scalability will be feasible using different scenarios.
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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 | 0.001 | 0.001 |
| 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