Joint Virtual Network Topology Design and Embedding for Cybertwin-Enabled 6G Core Networks
Bibliographic record
Abstract
To efficiently allocate heterogeneous resources for customized services, in this article, we propose a network virtualization (NV)-based network architecture in cybertwin-enabled 6G core networks. In particular, we investigate how to optimize the virtual network (VN) topology (which consists of several virtual nodes and a set of intermediate virtual links) and determine the resultant VN embedding in a joint way over a cybertwin-enabled substrate network. To this end, we formulate an optimization problem whose objective is to minimize the embedding cost, while ensuring that the end-to-end (E2E) packet delay requirements are satisfied. The queueing network theory is utilized to evaluate each service’s E2E packet delay, which is a function of the resources assigned to the virtual nodes and virtual links for the embedded VN. We reveal that the problem under consideration is formally a mixed-integer nonlinear program (MINLP) and propose an improved brute-force search algorithm to find its optimal solutions. To enhance the algorithm’s scalability and reduce the computational complexity, we further propose an adaptively weighted heuristic algorithm to obtain near-optimal solutions to the problem for large-scale networks. Simulations are conducted to show that the proposed algorithms can effectively improve network performance compared to other benchmark algorithms.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 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".