Cooperative Resource Allocation and Traffic Scheduling for IIoT Controllers in Edge Clouds: A Hierarchical Reinforcement Learning Approach
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".