AI-Driven Packet Forwarding With Programmable Data Plane: A Survey
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
The existing packet forwarding technology cannot meet the increasing requirements of Internet development due to its rigid framework. Application of artificial intelligence (AI) for efficient packet forwarding is gaining more and more interest as a new direction. Recently, the explosive development of programmable data plane (PDP) has provided a potential impetus to packet forwarding driven by AI. Therefore, this paper presents a survey on the recent research in AI-driven packet forwarding with PDP. First, we describe two of the most representative frameworks of the packet forwarding, i.e., the traditional AI-driven forwarding framework and the new one assisted by the PDP. Then, we focus on capacity of the packet forwarding under the two frameworks in four measures: delay, throughput, security, and reliability. For each measure, we organize the content with the evolution from simple packet forwarding, to packet forwarding capacity enhancement with the assistance of AI, to the latest research on AI-driven packet forwarding supported by the PDP. Finally, we identify three directions in the development of AI-driven packet forwarding, and highlight the challenges and issues in future research.
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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 | 0.013 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.011 | 0.005 |
| 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