Cooperative Resource Allocation and Traffic Scheduling for IIoT Controllers in Edge Clouds: A Hierarchical Reinforcement Learning Approach
Why this work is in the frame
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Bibliographic record
Abstract
Time-sensitive networking (TSN) standards have been gaining ground in Industry 4.0 for meeting the stringent Quality of Service (QoS) requirements for industrial applications. The trend of deploying industrial automation controllers in cloud environments has created a demand for TSN-enabled edge clouds. To this end, this paper proposes a Hierarchical Reinforcement Learning (HRL)-based Joint Processing Unit & Memory Allocation and Scheduling (HRL-JPUMAS) framework to meet the stringent bounded low latency and ultra-reliability needs of time-sensitive traffic in the cloud environments. The HRL's high-level computational resource allocation agent redistributes processing unit and memory resources among multiple IoT controllers. It is followed by a low-level custom traffic scheduling algorithm for Time Aware Shaper (TAS) in IEEE 802.1 Qbv standard to manage the transmission of generated traffic from executed tasks. The HRL-JPUMAS exhibits significant performance improvements in maximizing executed tasks and scheduled frames compared with First Come First Serve, Proportional Fairness algorithms and employing independent Deep Q-Networks (DQN) for these two tasks.
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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 | 0.001 |
| 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