Distributed Resource Allocation Optimization in 5G Virtualized Networks
Bibliographic record
Abstract
The concepts of network function virtualization and end-to-end network slicing are the two promising technologies empowering 5G networks for efficient and dynamic network/service deployment and management. 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 virtual network functions. We first consider a system of collaborative slices and formulate the resource allocation as a convex optimization problem, maximizing the overall system utility function. We further introduce a distributed solution for the resource allocation problem by forming a resource auction between the slices and the data centers. By using an example, we show how the selfish behavior of non-collaborative slices affects the fairness performance of the system. For a system with non-collaborative slices, we formulate a new resource allocation problem based on the notion of dominant resource fairness and propose a fully distributed scheme for solving the problem. Simulation results are provided to show the validity of the results, evaluate the convergence of the distributed solutions, show protection of collaborative slices against non-collaborative slices and compare the performance of the optimal schemes with the 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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 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".