Towards an End-to-End Network Slicing Framework in Multi-Region Infrastructures
Bibliographic record
Abstract
End-to-end network slicing is a promising concept based on softwarization and virtualization, leading the way towards efficiently achieving the network and operational key performance indicators (KPIs) for future wireless systems that comprise softwarized network functions. It leverages the underlying physical infrastructure to create and orchestrate agile and programmable network functions which satisfy the end-to-end user demands. These features are crucial during situations in which sudden demand surges stress the wireless system. Proper network orchestration can provide the necessary and timely adaptability to offer sustained communication between end-users. This paper addresses the design and implementation of an end-to-end network slicing framework specifically designed to provide orchestration tools for softwarized network functions, in order to fulfill the system requirements for surge events such as flash crowds. The conducted performance evaluations demonstrate the applicability of this approach and validate the proof-of-concept implementation.
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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 | 0.001 | 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".