A Service Function Chain Traffic Steering Path Algorithm Based on Graph Convolutional Network and Deep Q-Network
Why this work is in the frame
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Bibliographic record
Abstract
With the rise of applications such as the Internet of Things (IoT) and Virtual Reality (VR), there is an increasing demand for stringent service latency and quality of service requirements, which has led to a shift in network service deployment from cloud to edge, giving rise to Mobile Edge Computing (MEC) architectures.In MEC environments, network infrastructure is distributed near users, allowing access to local networks in real time.However, dynamically orchestrating Service Function Chains (SFCs) presents a significant challenge, especially in resource-constrained settings where maximizing SFC deployments while maintaining low latency is essential for service providers' revenue optimization.To address this challenge, this paper proposes an intelligent SFC orchestration strategy, termed GCN-DQN, which combines Graph Convolutional Networks (GCNs) and Deep Q-Networks (DQNs).The GCN-DQN framework is designed to optimize the request acceptance rate while ensuring compliance with stringent low-latency requirements.To achieve this, the GCN-DQN strategy is designed to perceive network structure, resource availability, and SFCspecific information, enabling optimized decision-making for SFC traffic steering paths.Performance evaluations demonstrate that the proposed algorithm outperforms existing methods in SFC request acceptance rates under various network loads.
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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 | 0.000 |
| 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