Bibliographic record
Abstract
The concepts of network function virtualization (NFV) and end-to-end (E2E) network slicing are two promising technologies empowering 5G networks for efficient, flexible and dynamic network deployment and service management. Optimal resource allocation is one of the challenging problems to address in such networks. In this paper, we propose a resource allocation model for 5G virtualized networks in a heterogeneous cloud infrastructure. In our model, each network slice has a resource demand vector for each of its building virtual network functions (VNFs). We then formulate the optimal resource allocation as a convex optimization problem maximizing the overall system utility as a function of the slice thicknesses with the constraints of the data centers’ resource capacities. The slice thickness variables together with the demand vectors determine the amount of resources allocated to each slice. We further propose a distributed solution for the resource allocation problem based on auction/game theory by forming a resource auction between the slices and the data centers (DCs). It is shown that the resource allocation game has a unique Nash equilibrium and its solution is the same as the solution of the centralized system optimization problem, i.e., in equilibrium the slice thicknesses maximize the overall system utility. Numerical analysis are provided to show the validity of the results, evaluate the convergence of the distributed solution and also comparing the performance of the optimal scheme with heuristic ones.
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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 | 0.002 |
| 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".