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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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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.

We present Brown Dog, two highly extensible services that aim to leverage any existing pieces of code, libraries, services, or standalone software (past or present) towards providing users with a simple to use and programmable means of automated aid in the curation and indexing of distributed collections of uncurated and/or unstructured data. Data collections such as these encompassing large varieties of data, in addition to large amounts of data, pose a significant challenge within modern day "Big Data" efforts. The two services, the Data Access Proxy (DAP) and the Data Tilling Service (DTS), focusing on format conversions and content based analysis/extraction respectively, wrap relevant conversion and extraction operations within arbitrary software, manages their deployment in an elastic manner, and manages job execution from behind a deliberately compact REST API. We describe both the motivation and need/scientific drivers for such services, the constituent components that allow for arbitrary software/code to be used and managed, and lastly an evaluation of the systems capabilities and scalability.