End-to-End Delay Modeling for Embedded VNF Chains in 5G Core Networks
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Bibliographic record
Abstract
In this paper, an analytical end-to-end (E2E) packet delay modeling is established for multiple traffic flows traversing an embedded virtual network function (VNF) chain in fifth generation communication networks. The dominant-resource generalized processing sharing is employed to allocate both computing and transmission resources among flows at each network function virtualization (NFV) node to achieve dominant-resource fair allocation and high resource utilization. A tandem queueing model is developed to characterize packets of multiple flows passing through an NFV node and its outgoing transmission link. For analysis tractability, we decouple packet processing (and transmission) of different flows in the modeling and determine average packet processing and transmission rates of each flow as approximated service rates. An M/D/1 queueing model is developed to calculate packet delay for each flow at the first NFV node. Based on the analysis of packet interarrival time at the subsequent NFV node, we adopt an M/D/1 queueing model as an approximation to evaluate the average packet delay for each flow at each subsequent NFV node. The queueing model is proved to achieve more accurate delay evaluation than that using a G/D/1 queueing model. Packet transmission delay on each embedded virtual link between consecutive NFV nodes is also derived for E2E delay calculation. Extensive simulation results demonstrate the accuracy of our proposed E2E packet delay modeling, upon which delay-aware VNF chain embedding can be achieved.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 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 it