Dynamic Property Enforcement in Programmable Data Planes
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
Network programmers can currently deploy an arbitrary set of protocols in forwarding devices through data plane programming languages such as P4. However, as any other type of software, P4 programs are subject to bugs and misconfigurations. Network verification tools have been proposed as a means of ensuring that the network behaves as expected, but these tools frequently face severe scalability issues. In this paper, we argue for a novel approach to this problem. Rather than statically inspecting a network configuration looking for bugs, we propose to enforce networking properties at runtime. To this end, we developed P4box, a system for deploying runtime monitors in programmable data planes. P4box allows programmers to easily express a broad range of properties (both program-specific and network-wide). Moreover, we provide an automated framework based on assertions and symbolic execution for ensuring monitor correctness. Our experiments on a SmartNIC show that P4box monitors represent a small overhead to network devices in terms of latency, throughput and power consumption.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.002 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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