Learning-Based Transmission Protocol Customization for VoD Streaming in Cybertwin-Enabled Next-Generation Core Networks
Bibliographic record
Abstract
Next-generation core networks are expected to achieve service-oriented traffic management for diversified Quality-of-Service (QoS) provisioning based on software-defined networking (SDN) and network function virtualization (NFV). In this article, a learning-based transmission protocol customized for Video-on-Demand (VoD) streaming services is proposed for a Cybertwin-enabled next-generation core network, which provides caching-based congestion control and throughput enhancement functionalities at the edge of the core network based on traffic prediction. The per-slot traffic load of a VoD streaming service at an ingress edge node is predicted based on the autoregressive integrated moving average (ARIMA) model. To balance the tradeoff between network congestion and throughput enhancement, a multiarmed bandit (MAB) problem is formulated to maximize the expected overall network performance in a long run, by capturing the relationship between transmission control actions and QoS provisioning. A comprehensive transmission protocol operation framework is also presented with in-network congestion control and throughput enhancement modules. Simulation results are presented to validate the efficacy of the proposed protocol in terms of packet delay, goodput ratio, throughput, and resource utilization.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| 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 | 0.001 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), 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".