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.