NV: an intermediate language for verification of network control planes

Giannarakis, Nick; Loehr, Devon; Beckett, Ryan; Walker, David

doi:10.1145/3385412.3386019

Cited by 23 publications

(36 citation statements)

References 14 publications

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“…Network analysis tools such as Batfish [Fogel et al 2015] and NV [Giannarakis et al 2020] operate by translating the domain-specific languages for configuring routers, such as those produced by CISCO and Juniper, into intermediate modeling languages for analysis. Our tool, ProbNV, uses the translation infrastructure developed for NV, and most of its functional intermediate language for modeling network control planes.…”

Section: Key Ideasmentioning

confidence: 99%

“…Past control plane simulators, such as Batfish [Fogel et al 2015], NV [Giannarakis et al 2020] and FastPlane [Lopes and Rybalchenko 2019] have operated over concrete networks. 4 ProbNV differs by making it possible to simulate models that include symbolic and probabilistic hypotheses.…”

Section: Efficient Symbolic Simulationmentioning

confidence: 99%

“…ProbNV's implementation consists of two main components: (1) a configuration compiler that translates CISCO configurations for BGP and OSPF protocols into a functional and probabilistic network modeling language, and (2) a symbolic simulation engine that executes the resulting functional models efficiently and is optimized for network control plane models. The configuration compiler is not novelÐwe reuse most of an existing compiler [Giannarakis et al 2020]. Our main technical achievement comes in the design and implementation of the symbolic simulator.…”

Section: Introductionmentioning

confidence: 99%

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ProbNV: probabilistic verification of network control planes

Giannarakis

Silva

Walker

2021

Proc. ACM Program. Lang.

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ProbNV is a new framework for probabilistic network control plane verification that strikes a balance between generality and scalability. ProbNV is general enough to encode a wide range of features from the most common protocols (eBGP and OSPF) and yet scalable enough to handle challenging properties, such as probabilistic all-failures analysis of medium-sized networks with 100-200 devices. When there are a small, bounded number of failures, networks with up to 500 devices may be verified in seconds. ProbNV operates by translating raw CISCO configurations into a probabilistic and functional programming language designed for network verification. This language comes equipped with a novel type system that characterizes the sort of representation to be used for each data structure: concrete for the usual representation of values; symbolic for a BDD-based representation of sets of values; and multi-value for an MTBDD-based representation of values that depend upon symbolics. Careful use of these varying representations speeds execution of symbolic simulation of network models. The MTBDD-based representations are also used to calculate probabilistic properties of network models once symbolic simulation is complete. We implement the language and evaluate its performance on benchmarks constructed from real network topologies and synthesized routing policies.

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Section: Key Ideasmentioning

confidence: 99%

Section: Efficient Symbolic Simulationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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ProbNV: probabilistic verification of network control planes

Giannarakis

Silva

Walker

2021

Proc. ACM Program. Lang.

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“…A key reason is that network verification analyses often manipulate sets of objects (e.g., packets) whereas these IVLs work via compilation to logical constraints (which help find a counter example). There is a recent IVL for networks, called NV [13]. It too cannot express a wide range of analyses.…”

Section: Motivationmentioning

confidence: 99%

“…Zen shares the linguistic modeling approach of NV [13]. While NV provides high level abstractions for encoding certain network functionalities (e.g., distributed routing) and analyses, Zen's abstractions are lower-level and more general.…”

Section: Related Workmentioning

confidence: 99%

A General Framework for Compositional Network Modeling

Beckett

Mahajan

2020

Proceedings of the 19th ACM Workshop on Hot Topics in Networks

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We advocate for an approach to network modeling and analysis based on a common intermediate language. Unlike today, where each tool builds a custom model and analysis engine for its target network functionality, we argue that network functionality should be expressed in a common language. This approach makes it easier to expand formal analysis to new functionality and analyze interactions between dependent functionalities (e.g., routing and packet filtering). We demonstrate the feasibility of this approach by developing an intermediate language called Zen and three different analyses for programs in that language. For representative data plane and control plane functionalities, we find that Zen reduces the modeling effort by an order of magnitude, while providing analysis performance that matches custom tools. CCS CONCEPTS • Networks → Network reliability; • Theory of computation → Program verification; • Software and its engineering → Model checking; Functional languages.

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