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A General Approach to Network Configuration Verification
Abstract
We present Minesweeper, a tool to verify that a network satisfies a wide range of intended properties such as reachability or isolation among nodes, waypointing, black holes, bounded path length, load-balancing, functional equivalence of two routers, and fault-tolerance. Minesweeper translates network configuration files into a logical formula that captures the stable states to which the network forwarding will converge as a result of interactions between routing protocols such as OSPF, BGP and static routes. It then combines the formula with constraints that describe the intended property. If the combined formula is satisfiable, there exists a stable state of the network in which the property does not hold. Otherwise, no stable state (if any) violates the property. We used Minesweeper to check four properties of 152 real networks from a large cloud provider. We found 120 violations, some of which are potentially serious security vulnerabilities. We also evaluated Minesweeper on synthetic benchmarks, and found that it can verify rich properties for networks with hundreds of routers in under five minutes. This performance is due to a suite of model-slicing and hoisting optimizations that we developed, which reduce runtime by over 460x for large networks.
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Agentic NetOps and AIOps systems differ mainly in their permitted authority.
“Agentic NetOps and AIOps systems differ mainly in their permitted authority [55], [67], [76].”
From Large language models for agentic NetOps and AIOps: Architectures, evaluation, and safety (2027) · cites this paper as [67]
The Co-pilot pattern improves operator throughput by compressing and navigating evidence.
“The Co-pilot pattern improves operator throughput by compressing and navigating evidence.”
From Large language models for agentic NetOps and AIOps: Architectures, evaluation, and safety (2027) · cites this paper as [67]
LLMs can help in log-based AIOps by mapping heterogeneous log language into more stable schemas, condensing long bursts into incident-focused summaries, and suggesting the next query or filter needed to test a hypothesis.
“LLMs can help here by mapping heterogeneous log language into more stable schemas, condensing long bursts into incidentfocused summaries, and suggesting the next query or filter needed to test a hypothesis [8], [67], [10].”
From Large language models for agentic NetOps and AIOps: Architectures, evaluation, and safety (2027) · cites this paper as [67]
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Metadata
- Authors
- Beckett, R., Gupta, A., Mahajan, R., & Walker, D.
- Affiliations
- Princeton University (US)
- Microsoft Research & Intentionet
- Venue
- Proceedings of the Conference of the ACM Special Interest Group on Data Communication
- Publisher
- ACM
- Published
- 2017-08-07
- Pages
- 155-168
- OpenAlex
- W2742515467
- Cited by
- 262
- References
- 0
- Citations by year
- 2026: 42 · 2025: 35 · 2024: 37 · 2023: 26 · 2022: 26 · 2021: 32 · 2020: 27 · 2019: 18 · 2018: 15 · 2017: 4
- FWCI
- 18.16
- Type
- conference paper
- Language
- en
- Open access
- closed
- Locations
- ACM SIGCOMM Conference (SIGCOMM) (publishedVersion)
- Funders
- National Science Foundation (1703493,1525936)
- Topics
- Physical Sciences › Computer Science › Computational Theory and Mathematics › Formal Methods in Verification
- Physical Sciences › Computer Science › Software › Software Testing and Debugging Techniques
- Physical Sciences › Computer Science › Computer Networks and Communications › Software-Defined Networks and 5G
- Keywords
- Minesweeper, satisfiability checking, reachability, black holes, load balancing, fault tolerance, model slicing, network security vulnerabilities, cloud networks
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