Priority-Aware Task Scheduling in Computing Power Network-Enabled Edge Computing Systems
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
The Internet of everything, a potential direction for the next-generation Internet, positions edge collaboration as a promising computing paradigm to address the workload dispersion and resource constraints inherent in traditional edge computing frameworks. However, the increasing complexity of cross-domain networks introduces challenges for efficient task execution and balanced resource utilization in edge collaboration, which remain insufficiently explored. To address these challenges, a next-generation network architecture, the compute power network (CPN), was recently proposed. The CPN leverages ubiquitous connections among heterogeneous resources to optimize task scheduling collaboratively. Building on this concept, we design an edge computing system that integrates CPN to enable dynamic and collaborative task scheduling. Inspired by the sliding window, we develop a dynamic scheduling scheme that prioritizes computing tasks and matches tasks to computing resources in real time. Additionally, we propose an improved deep reinforcement learning (DRL) algorithm to optimize scheduling policies, aiming to improve task success rates, minimize execution delays, and ensure balanced and efficient resource utilization. Lastly, simulation experiments validate the effectiveness of the proposed scheme and algorithm.
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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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.004 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 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