EP4: An Application-Aware Network Architecture with a Customizable Data Plane
Bibliographic record
Abstract
Fast and customizable programmable data planes (PDPs) implementing new services such as multi-flow synchronization, on- and in-time delivery, and in-network caching and compression are key enablers to future applications (e.g., streamed holograms, telesurgery, and autonomous industrial systems). This paper outlines the design principles of EP4, an application-aware extended P4-based network architecture that offers hosted applications an extensible catalog of services through its control plane. The latter configures a PDP that can achieve minimal parsing and processing for fast-tracked packets as well as customized processing and forwarding for other packets. An extended parser (eParser) performs the first task, which reduces the necessary latency experienced by packets. Alternatively, adaptive processing is achieved using an enhanced processor (eProcessor) that optionally parses customized headers using just-in-time programmable parsers. It then executes selected P4 packet processing pipelines implementing different services. These programs are installed at runtime without impacting other switch functionalities. Experimental results demonstrate the architecture's enhanced performance compared to current solutions.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".