Cantor meets Scott: semantic foundations for probabilistic networks. ProbNetKAT is a probabilistic extension of NetKAT with a denotational semantics based on Markov kernels. The language is expressive enough to generate continuous distributions, which raises the question of how to compute effectively in the language. This paper gives an new characterization of ProbNetKAT’s semantics using domain theory, which provides the foundation needed to build a practical implementation. We show how to use the semantics to approximate the behavior of arbitrary ProbNetKAT programs using distributions with finite support. We develop a prototype implementation and show how to use it to solve a variety of problems including characterizing the expected congestion induced by different routing schemes and reasoning probabilistically about reachability in a network.
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References in zbMATH (referenced in 4 articles , 1 standard article )
Showing results 1 to 4 of 4.
- Wang, Di; Hoffmann, Jan; Reps, Thomas: A denotational semantics for low-level probabilistic programs with nondeterminism (2019)
- Dahlqvist, Fredrik; Silva, Alexandra; Danos, Vincent; Garnier, Ilias: Borel kernels and their approximation, categorically (2018)
- Kahn, David M.: Undecidable problems for probabilistic network programming (2017)
- Smolka, Steffen; Kumar, Praveen; Foster, Nate; Kozen, Dexter; Silva, Alexandra: Cantor meets Scott: semantic foundations for probabilistic networks (2017)