An improved resource scheduling strategy through concatenated deep learning model for edge computing IoT networks
Bibliographic record
Abstract
Summary With increasing challenges and research in edge‐assisted IoT models, an improved resource scheduling approach exploiting deep learning concepts is proposed in this research work. Improvement in performance in the proposed work is achieved primarily by addressing the response time and waiting time. This could be achieved if the optimal resources are scheduled without any delay. The presented concatenated deep learning technique considers the time series IoT network source requirements and allocates optimal resources from the resource pool, considering resource availability, workload, and computation time. Two deep learning techniques, namely, CNN and GRU, are utilized for the concatenation process, while resource characteristics are considered as features that are extracted and classified to schedule optimal resources. Novelty in the proposed work is exhibited in the form of the concatenation process proposed. The proposed resource scheduling performance metrics are compared with the performance of the existing scheduling model through simulation analysis for better validation. The proposed model selects the optimal resources from the resource pool using concatenated features and schedules for respective requests with minimum delay and waiting time, which increases the overall efficiency of the edge computing IoT networks.
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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 | 0.000 | 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 | 0.001 | 0.001 |
| Research integrity | 0.000 | 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".