Learning-Assisted Dynamic VNF Selection and Chaining for 6G Satellite-Ground Integrated Networks
Bibliographic record
Abstract
The sixth generation (6G) mobile communication system is expected to provide global seamless network coverages, where a satellite-ground integrated network (SGIN) is seen as one of the typical 6G networking paradigms. In this paper, a dynamic virtual network function (VNF) selection and chaining (DVSC) problem in an SGIN is investigated. We aim to balance the network resource provisioning and VNF migration costs with service performance gain to maximize the long-term network profit. Specifically, we formulate the DVSC problem as a Markov decision process (MDP), by taking into consideration the heterogeneity and time-varying nature of SGINs. A novel VNF selection and chaining scheme is proposed, where a deepQ-learning (DQL) algorithm is designed to dynamically determine a set of VNF selection and chaining policies (VSCPs) based on the evolving network states (e.g., network resources, network topology, and network traffic load). Furthermore, to elaborate the level of computing resource sharing of VSCP sets, a new sharing ratio (SR) is proposed. To efficiently allocate heterogeneous network resources, the action space is built by clustering the historical records of the network load and selecting the VSCP set for each cluster in a greedy manner. Extensive simulation results are presented to demonstrate the effectiveness of the proposed framework in comparison with the state-of-the-art schemes.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 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) | 0.002 | 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".