Microservice-based Network Digital Twins: A Slicing Approach
Bibliographic record
Abstract
The Network Digital Twins (NDTs) have become a frontier thanks to their real-time monitoring, analysis, prediction, and optimisation capabilities. However, their architecture has not been designed for the scale of next-generation networks that will ensure ubiquitous connectivity. At this, different NDT applications may require distinct levels of quality of service requirements to be met. With increasing size and heterogeneity in the networks, the processing load at the NDTs escalates, and these requirements may not be met. Therefore, we propose a microservice-based architecture with application-oriented slicing. Here, we present the scaling methodology, which enables scaling at both microservice and slice levels. Then, we evaluate the throughput, delay, and quality of service requirement violation rate metrics under two scenarios. Thanks to the slicing approach with microservice-based implementation, the end-to-end delay and the QoS requirement violation rate are reduced while having higher throughput performance.
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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 | 0.001 | 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".