Design and Development of a Machine Learning-Based Task Orchestrator for Intelligent Systems on Edge Networks
Bibliographic record
Abstract
This paper proposes an edge-centric workload orchestration approach that uses machine learning in a three-tier vehicular architecture (edge, cloud via roadside unit, or cloud via cellular base station). The orchestrator uses a wireless network at the edge to receive and send over-the-air requests from vehicles, considering a metropolitan network connects the entire edge structure to support the high mobility of devices, allowing vehicles to share information and resources. Additionally, suppose the edge is congested, or its resources are unavailable. In that case, cloud resources will be used via roadside or cellular networks using a wide-area network to meet the tasks’ time constraints. Moreover, the proposed machine learning model uses variance-based sensitivity analysis to determine which inputs influence the model’s final decision. The experiments performed on the EdgeCloudSim simulator are based on modelling computational and network resources besides the representation of vehicles. The results indicate that our approach best fits task offloading over the air, outperforming the comparative experiments between the one-stage(our approach) model against two-stage and random models. Furthermore, by using our one-stage model that outputs the average of the prediction interval and the variance of this interval, we can measure how confident our model is in its prediction.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".