A Deep Reinforcement Learning With Transformer Integration for Directed Acyclic Graph Scheduling in Edge Networks
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Bibliographic record
Abstract
The rapid adoption of 5G technology and Internet of things (IoT) devices has fueled significant growth in intelligent applications, increasing their complexity beyond simple task definitions. Scheduling intelligent applications modeled as directed acyclic graphs (DAGs) has thus emerged as a crucial challenge. Our proposed solution is a deep reinforcement learning (DRL) framework that uniquely integrates proximal policy optimization (PPO) with a transformer-based module for scheduling DAG applications. Unlike other approaches that rely on predefined priorities or static optimization algorithms, our approach enables agents to autonomously explore task execution orders and dynamically adapt to changing network resource conditions, learning optimal scheduling strategies. The algorithm leverages transformers to handle complex task dependencies, minimizing application duration and user energy consumption by jointly optimizing application processing order, task priorities, transmit power, offloading decisions, and computational frequency. Through a series of simulations, we prove the effectiveness of the proposed algorithm and demonstrate the performance comparison under different settings, providing a more flexible and robust solution for DAG scheduling in edge networks.
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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 | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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