A GNN-DRL-based Collaborative Edge Computing Strategy for Partial Offloading
Bibliographic record
Abstract
Edge computing is an emerging distributed computing paradigm that reduces computation latency and energy consumption by offloading application tasks from user devices to near-edge servers for execution. In order to utilize the computing resources of edge servers and increase efficiency, collaborative edge computing is proposed as a new type of edge computing method where multiple edge servers can work together to solve a task. By dividing a task into interrelated subtasks, each subtask can be processed locally at the device or offloaded to an edge server to minimize processing latency. In the paper, we propose a GNN-DRL-based offloading strategy that considers the edge servers' task attributes and network topology to make an optimal offloading decision for each application subtask. Experiments show that our proposed method performs better than state-of-the-art and baseline strategies in reducing the average latency for each task.
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How this classification was reachedexpand
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".