Fix with P6

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Fix with P6

Verifying programmable switches at runtime

Conference Paper (2021)

Author(s)

Apoorv Shukla (Huawei European Research Center)

Kevin Hudemann (SAP AG)

Zsolt Vagi (Swisscom)

Lily Hugerich (Technical University of Berlin)

Georgios Smaragdakis (Technical University of Berlin, MPI-Informatics)

Artur Hecker (Huawei European Research Center)

Stefan Schmid (University of Vienna)

Anja Feldmann (MPI-Informatics)

Affiliation

External organisation

DOI related publication

https://doi.org/10.1109/INFOCOM42981.2021.9488772 Final published version

To reference this document use

https://resolver.tudelft.nl/uuid:0c233ea0-c784-46ee-901b-b9f1bf995b42

More Info

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Publication Year

2021

Language

English

Affiliation

External organisation

Article number

9488772

ISBN (electronic)

9780738112817

Event

40th IEEE Conference on Computer Communications, INFOCOM 2021 (2021-05-10 - 2021-05-13), Vancouver, Canada

Downloads counter

215

Abstract

We design, develop, and evaluate P6, an automated approach to (a) detect, (b) localize, and (c) patch 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: 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 such as switch.p4 without triggering any regressions.