Abstract Interpretation of Stateful Networks

Kalev Alpernas*, Roman Manevich, Aurojit Panda, Mooly Sagiv, Scott Shenker, Sharon Shoham, Yaron Velner

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

9 Scopus citations


Modern networks achieve robustness and scalability by maintaining states on their nodes. These nodes are referred to as middleboxes and are essential for network functionality. However, the presence of middleboxes drastically complicates the task of network verification. Previous work showed that the problem is undecidable in general and EXPSPACE-complete when abstracting away the order of packet arrival. We describe a new algorithm for conservatively checking isolation properties of stateful networks. The asymptotic complexity of the algorithm is polynomial in the size of the network, albeit being exponential in the maximal number of queries of the local state that a middlebox can do, which is often small. Our algorithm is sound, i.e., it can never miss a violation of safety but may fail to verify some properties. The algorithm performs on-the fly abstract interpretation by (1) abstracting away the order of packet processing and the number of times each packet arrives, (2) abstracting away correlations between states of different middleboxes and channel contents, and (3) representing middlebox states by their effect on each packet separately, rather than taking into account the entire state space. We show that the abstractions do not lose precision when middleboxes may reset in any state. This is encouraging since many real middleboxes reset, e.g., after some session timeout is reached or due to hardware failure.

Original languageEnglish
Title of host publicationStatic Analysis - 25th International Symposium, SAS 2018, Proceedings
EditorsAndreas Podelski
PublisherSpringer Verlag
Number of pages21
ISBN (Print)9783319997247
StatePublished - 2018
Event25th International Static Analysis Symposium, SAS 2018 - Freiburg, Germany
Duration: 29 Aug 201831 Aug 2018

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume11002 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349


Conference25th International Static Analysis Symposium, SAS 2018


FundersFunder number
European Union’s Seventh Framework Program321174-VSSC
National Science Foundation1704941, 1420064
Intel Corporation
Horizon 2020 Framework Programme759102
Blavatnik Family Foundation
European Research Council
United States-Israel Binational Science Foundation2016260, 2012259
Tel Aviv University
PAZY Foundation


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