3 research outputs found
Scalable verification of probabilistic networks
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