English
 
Help Privacy Policy Disclaimer
  Advanced SearchBrowse

Item

ITEM ACTIONSEXPORT

Released

Paper

Towards Runtime Verification of Programmable Switches

MPS-Authors
/persons/resource/persons211491

Feldmann,  Anja
Internet Architecture, MPI for Informatics, Max Planck Society;

External Resource
No external resources are shared
Fulltext (restricted access)
There are currently no full texts shared for your IP range.
Fulltext (public)

arXiv:2004.10887.pdf
(Preprint), 2MB

Supplementary Material (public)
There is no public supplementary material available
Citation

Shukla, A., Hudemann, K., Vági, Z., Hügerich, L., Smaragdakis, G., Schmid, S., et al. (2020). Towards Runtime Verification of Programmable Switches. Retrieved from http://arxiv.org/abs/2004.10887.


Cite as: http://hdl.handle.net/21.11116/0000-0007-0AAD-C
Abstract
Is it possible to patch software bugs in P4 programs without human involvement? We show that this is partially possible in many cases due to advances in software testing and the structure of P4 programs. Our insight is that runtime verification can detect bugs, even those that are not detected at compile-time, with machine learning-guided fuzzing. This enables a more automated and real-time localization of bugs in P4 programs using software testing techniques like Tarantula. Once the bug in a P4 program is localized, the faulty code can be patched due to the programmable nature of P4. In addition, platform-dependent bugs can be detected. From P4_14 to P4_16 (latest version), our observation is that as the programmable blocks increase, the patchability of P4 programs increases accordingly. To this end, we design, develop, and evaluate P6 that (a) detects, (b) localizes, and (c) patches bugs in P4 programs with minimal human interaction. P6 tests P4 switch non-intrusively, i.e., requires no modification to the P4 program for detecting and localizing bugs. We used a P6 prototype to detect and patch seven existing bugs in eight 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 P4 programs such as switch.p4 without triggering any regressions.