Runtime Verification for Programmable Switches

Journal article (2023)

Authors

Apoorv Shukla Huawei Technologies Deutschland GmbH
Zsolt Vagi Swisscom AG
Lily Hugerich Technical University of Berlin
Artur Hecker Huawei Technologies Deutschland GmbH
Stefan Schmid Technical University of Berlin
Anja Feldmann Max Planck Institut für Informatik
Copyright: © 2023 Apoorv Shukla, Kevin Hudemann, Zsolt Vagi, Lily Hugerich, G. Smaragdakis, Artur Hecker, Stefan Schmid, Anja Feldmann
DOI: https://doi.org/10.1109/TNET.2023.3234931
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Published Date

2023

Language

English

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Abstract

We introduce a runtime verification framework for programmable switches that complements static analysis. To evaluate our approach, we design and develop P6, a runtime verification system that automatically detects, localizes, and patches software bugs in P4 programs. Bugs are reported via a violation of pre-specified expected behavior that is captured by P6. P6 is based on machine learning-guided fuzzing that tests P4 switch non-intrusively, i.e., without modifying the P4 program for detecting runtime bugs. This enables an automated and real-time localization and patching of bugs. We used a P6 prototype to detect and patch existing bugs in various publicly available P4 application programs deployed on two different switch platforms, namely, behavioral model (bmv2) and Tofino. Our evaluation shows that P6 significantly outperforms bug detection baselines while generating fewer packets and patches bugs in large P4 programs, e.g., switch.p4 without triggering any regressions.

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