A General Framework for Compositional Network Modeling

Beckett, Ryan; Mahajan, Ratul

doi:10.1145/3422604.3425930

Cited by 10 publications

(4 citation statements)

References 39 publications

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“…Under the hood, TIMEPIECE uses Microsoft's Zen verification library [11], which in turn serves as an interface to the Z3 SMT solver. Hence, the only practical limits to a user's network model are those that arise from the features and theories supported by Z3.…”

Section: Methodsmentioning

confidence: 99%

Modular Control Plane Verification via Temporal Invariants

Alberdingk¹,

Beckett²,

Gupta³

et al. 2022

Preprint

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Satisfiability Modulo Theory (SMT)-based tools for network control plane analysis make it possible to reason exhaustively about interactions with peer networks and to detect vulnerabilities such as accidental use of a network as transit or prefix hijacking. SMT-based reasoning also facilitates synthesis and repair. To scale SMT-based verification to large networks, we introduce TIMEPIECE, a new modular control plane verification system. While past verifiers like Minesweeper [8] were based on analysis of stable paths, we show that such models, when deployed naïvely in service of modular verification, are unsound. To rectify the situation, we adopt a routing model based around a logical notion of time and develop a sound, expressive, and scalable verification engine.Our system requires that a user specifies interfaces between module components. We develop methods for defining these interfaces using predicates inspired by temporal logic, and show how to use those interfaces to verify a range of networkwide properties such as reachability, "no transit," and "no hijacking." Verifying a prefix-filtering policy using a nonmodular verification engine times out on a 320-node fattree network after 4 hours. However, TIMEPIECE verifies a 4,500node fattree in 6.5 minutes on a 96-core virtual machine. Modular verification of individual routers is embarrassingly parallel and completes in seconds, which allows verification to scale beyond non-modular engines, while still allowing the full power of SMT-based symbolic reasoning.

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Section: Methodsmentioning

confidence: 99%

Modular Control Plane Verification via Temporal Invariants

Alberdingk¹,

Beckett²,

Gupta³

et al. 2022

Preprint

Self Cite

View full text Add to dashboard Cite

show abstract

“…Zen is a modeling language that allows one to express and analyze a wide variety of network functions written in C# [6]. Composing two Zen models is purely operational in that a function of one model can call a function of the other.…”

Section: Related Workmentioning

confidence: 99%

Network Programming via Computable Products

Volpano¹

2022

Preprint

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The User Plane Function (UPF) aims to provide network services in the 3GPP 5G core network. These services need to be implemented on demand inexpensively with provable properties. Existing network dataplane programming languages are not up to the task. A new software paradigm is presented for the UPF. It is inspired by model checking a concurrent reactive system where conceptually each component of the system is modeled as an extended finitestate machine and their product is verified. We show how such a product can be computed for one example of a UPF and how its state invariants can be inferred, thereby eliminating the need to formally verify the product separately. Code can be generated from the product and regenerated on the fly to remain optimal for the probability distribution of network traffic the UPF must process.

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“…Binary Decision Diagrams (BDDs) [Bryant 1986] have enjoyed widespread adoption in the verification community thanks to their succinct representation of large sets of values and the (relatively) efficient implementation of operations between such sets. Network verification is no exception to this as prior work has demonstrated [Beckett et al 2019a;Beckett and Mahajan 2020;Giannarakis et al 2020;Smolka et al 2019]. In section 2.2, we showed how we use BDDs to implement boolean symbolic values and operations over them.…”

Section: Interpreting Symbolic Expressionsmentioning

confidence: 99%

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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A General Framework for Compositional Network Modeling

Cited by 10 publications

References 39 publications

Modular Control Plane Verification via Temporal Invariants

Modular Control Plane Verification via Temporal Invariants

Network Programming via Computable Products

ProbNV: probabilistic verification of network control planes

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