Proactive Service Assurance in 5G and B5G Networks: A Closed-Loop Algorithm for End-to-End Network Slices
Bibliographic record
Abstract
Ensuring the highest levels of performance and reliability for customized services in fifth-generation (5G) and beyond (B5G) networks requires the automation of resource management within network slices. In this paper, we propose PCLANSA, a proactive closed-loop algorithm that dynamically allocates and scales resources to meet the demands of diverse applications in real time for an end-to-end (E2E) network slice. In our experiment, PCLANSA was evaluated to ensure that each virtual network function is allocated the resources it requires, thereby maximizing efficiency and minimizing waste. This goal is achieved through the intelligent scaling of virtual network functions. The benefits of PCLANSA have been demonstrated across various network slice types, including eMBB, mMTC, uRLLC, and VoIP. This finding indicates the potential for substantial gains in resource utilization and cost savings, with the possibility of reducing over-provisioning by up to 54.85%.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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 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".