Joint Resource Allocation and Online Virtual Network Embedding for 5G Networks
Bibliographic record
Abstract
Next generation (5G) wireless networks are expected to accommodate proliferation of connected devices and multimedia services. To support multimedia services in an agile, cost-effective, and flexible way, network virtualization is a potential solution. This paper investigates service- oriented network virtualization for 5G wireless networks, to efficiently allocate heterogeneous resources to accommodate multimedia services. Specifically, we study joint resource allocation for virtual network requests (VNRs) and online embedding the resultant VNRs in core networks (CNs). With the deployment of multiple traffic aggregation points (TAPs) in radio access networks (RANs), the end-to- end traffic from heterogeneous access technologies can be aggregated and then grouped based on their destinations. Queueing models are developed in determining the minimal capacity required at each core network element. Virtual network embedding (VNE) in the core network is further proposed to achieve efficient physical resource sharing in CNs. Simulation results validate the VNE process in core networks based on the optimized capacities.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| 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".