Softwarized Resource Allocation in Digital Twins-Empowered Networks for Future Quantum-Enabled Consumer Applications
Why this work is in the frame
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Bibliographic record
Abstract
Network softwarization (NetSoft), recognized as crucial attribute of 6G networks, promises to provide enhanced and advanced services, including future quantum-enabled consumer applications. Softwarized resource allocation is the core issue in NetSoft concept. Digital twins (DT) guarantees to generate the corresponding digital world that reflects and interacts with the original physical world seamlessly. With DT empowering, the digital replica of softwarized networks can be generated to predict, simulate, analyze the softwarized resource allocation in more economical, convenient and scalable methods.In this paper, we research the softwarized resource allocation of requested services, usually, called as slices, in DT-empowered networks for future quantum-enabled consumer applications. We focus on developing efficient softwarized resource allocation algorithm. At first, we present models of the DT-empowered networks and service requests by using graph theory and hypergraph theory. Then, we design one softwarized resource management framework, labeled as DT-Slice-Soft-6G. This framework has the functions of managing softwarized resources, calculating resource allocation solution in digital replica and sending the calculated solution back to softwarized 6G networks. Thereafter, one efficient and fine-grained softwarized resource allocation algorithm, inserted in DT-Slice-Soft-6G, is detailed. This algorithm is labeled as Heu-DT-Slice-6G and is proposed based on efficient heuristic methods. To validate the highlights of DT-Slice-Soft-6G and Heu-DT-Slice-6G, we conduct the simulation work in our self-developed simulator.
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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 | 0.002 |
| 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