Load-Aware Network Resource Orchestration in LEO Satellite Network: A GAT-Based Approach
Why this work is in the frame
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Bibliographic record
Abstract
As an integral component of the space-air-ground integrated network (SAGIN), the low Earth orbit (LEO) satellite network has displayed immense potential in providing ubiquitous connectivity and broadband mobile communication. However, the intrinsic dynamics of LEO satellites pose unprecedented challenges in network management and service delivery. In this paper, we investigate the service function chain (SFC) orchestration in dynamic LEO satellite networks to achieve flexible and efficient service provision. Considering the service requirements and the limitations of network resources, we formulate the SFC orchestration problem as the integer nonlinear programming (INLP) problem for maximizing the service acceptance and the load fairness of satellites. Then, an efficient heuristic algorithm is proposed to solve this problem. Addressing the situation with frequent service requests, a graph attention network (GAT)-based approach with low complexity is also presented. Simulation results demonstrate that our proposed approaches outperform the benchmarks by a substantial margin in terms of load fairness and service acceptance. Besides, the proposed GAT-based approach shows its advantage in computation complexity, and exhibits robustness in unstable network scenarios with intermittent link interruptions.
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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 | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 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