A Reliable AMF Scaling and Load Balancing Framework for 5G Core Networks
Bibliographic record
Abstract
Fifth Generation (5G) networks have revolutionized modern networking practices by supporting an increasing number of connected devices and delivering improved performance through higher data rates and diverse application support. Enabling technologies are crucial to the development of evolving 5G networks to address user demand; however, the strategies they employ and the solutions they implement must account for the optimization of the network resources at hand. To this end, the work presented in this paper outlines a reliable Access and Mobility Function (AMF) scaling and load balancing framework that uses traffic class distributions and weights to determine the minimum number of AMF instances required to meet the projected demand and the relative capacity of the AMF instances to perform load balancing. The work outlined in this paper was conducted by creating a 5G core prototype using open-source emulation software. The presented results demonstrate how the resilience of the solution is controlled through the optimization problem formulation, and the load balancing module effectively balances the load across all instances in a set of AMFs.
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How this classification was reachedexpand
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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".