Service-Oriented Network Resource Orchestration in Space-Air-Ground Integrated Network
Bibliographic record
Abstract
Space-air-ground integrated networks (SAGINs) are envisioned to provide seamless coverage and enhanced flexibility compared with traditional terrestrial mobile networks, which has attracted much attention from both industry and academia. However, orchestrating heterogeneous resources in such a large-scale and dynamic network is challenging, especially encountering diverse services with multi-dimensional requirements. In this paper, we first propose a software-defined networking (SDN) and network function virtualization (NFV)-based reconfigurable SAGIN architecture for constructing service function chains (SFCs). Based on that, we investigate the SFC orchestration and wireless resource management where the virtual link rate adaption between each virtual network function (VNF) is introduced to improve the network resource utilization. Considering the limited physical resource and the heterogeneity in SAGINs, we jointly formulate the VNF embedding, virtual link rate adaption, and wireless resource allocation as a mixed-integer nonlinear programming (MINLP) problem to maximize the network profit. Due to the NP-hardness of the problem, we first transform the problem into a continuous optimization problem by successive convex approximation. By introducing an additional penalty into the objective function, an iterative alternation algorithm is proposed to find a near-optimal solution of the transformed problem. Extensive simulation results show that our proposed approach outperforms the benchmarks in average network revenue, successfully serving probability, and resource consumption.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 source (direct Gemma or distilled Codex), 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".