Name
Abstract | ||
|---|---|---|
This paper presents McNetKAT, a scalable tool for verifying probabilistic network programs. McNetKAT is based on a new semantics for the guarded and history-free fragment of Probabilistic NetKAT in terms of finite-state, absorbing Markov chains. This view allows the semantics of all programs to be computed exactly, enabling construction of an automatic verification tool. Domain-specific optimizations and a parallelizing backend enable McNetKAT to analyze networks with thousands of nodes, automatically reasoning about general properties such as probabilistic program equivalence and refinement, as well as networking properties such as resilience to failures. We evaluate McNetKAT's scalability using real-world topologies, compare its performance against state-of-the-art tools, and develop an extended case study on a recently proposed data center network design.
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Year | DOI | Venue |
|---|---|---|
2019 | 10.1145/3314221.3314639 | Proceedings of the 40th ACM SIGPLAN Conference on Programming Language Design and Implementation |
Keywords | Field | DocType |
Network verification, Probabilistic Programming | Network planning and design,Computer science,Markov chain,Network topology,Theoretical computer science,Equivalence (measure theory),Probabilistic logic,Data center,Semantics,Scalability | Journal |
Volume | ISSN | ISBN |
abs/1904.08096 | In Proceedings of the 40th ACM SIGPLAN Conference on Programming
Language Design and Implementation (PLDI '19), June 22-26, 2019, Phoenix, AZ,
USA. ACM, New York, NY, USA | 978-1-4503-6712-7 |
Citations | PageRank | References |
0 | 0.34 | 0 |
Authors | ||
7 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Steffen Juilf Smolka | 1 | 11 | 3.43 |
| Praveen Kumar | 2 | 63 | 5.42 |
| David M. Kahn | 3 | 0 | 0.34 |
| Nate Foster | 4 | 2 | 2.47 |
| Justin Hsu | 5 | 364 | 24.38 |
| Dexter Kozen | 6 | 0 | 0.34 |
| Alexandra Silva | 7 | 20 | 6.76 |