SDATP: An SDN-Based Traffic-Adaptive and Service-Oriented Transmission Protocol
Bibliographic record
Abstract
In this paper, a software-defined networking (SDN) based adaptive transmission protocol (SDATP) is proposed to support applications in fifth generation (5G) communication networks. For time-critical services, such as machine-type communication services for industrial automation, high reliability and low latency are required. To guarantee the strict service requirements, a slice-level customized protocol is developed with in-network intelligence, including in-path caching-based retransmission and in-network congestion control. To further reduce the delay for end-to-end (E2E) service delivery, we jointly optimize the placement of caching functions and packet caching probability, which reduces E2E delay by minimizing retransmission hops. Since the joint optimization problem is NP-hard, we transform the original problem to a simplified form and propose a low-complexity heuristic algorithm to solve the simplified problem. Numerical results are presented to validate the proposed probabilistic caching algorithm, including its adaptiveness to network dynamics and its effectiveness in reducing retransmission hops. Simulation results demonstrate the advantages of the proposed SDATP over the conventional transport layer protocol with respect to E2E packet delay.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".