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We present Leapfrog, a Coq-based framework for verifying equivalence of network protocol parsers. Our approach is based on an automata model of P4 parsers, and an algorithm for symbolically computing a compact representation of a bisimulation, using "leaps." Proofs are powered by a certified compilation chain from first-order entailments to low-level bitvector verification conditions, which are discharged using off-the-shelf SMT solvers. As a result, parser equivalence proofs in Leapfrog are fully automatic and push-button. We mechanically prove the core metatheory that underpins our approach, including the key transformations and several optimizations. We evaluate Leapfrog on a range of practical case studies, all of which require minimal configuration and no manual proof. Our largest case study uses Leapfrog to perform translation validation for a third-party compiler from automata to hardware pipelines. Overall, Leapfrog represents a step towards a world where all parsers for critical network infrastructure are verified. It also suggests directions for follow-on efforts, such as verifying relational properties involving security.

P4 is a domain-specific language for programming and specifying packet-processing systems. It is based on an elegant design with high-level abstractions like parsers and match-action pipelines that can be compiled to efficient implementations in software or hardware. Unfortunately, like many industrial languages, P4 has developed without a formal foundation. The P4 Language Specification is a 160-page document with a mixture of informal prose, graphical diagrams, and pseudocode, leaving many aspects of the language semantics up to individual compilation targets. The P4 reference implementation is a complex system, running to over 40KLoC of C++ code, with support for only a few targets. Clearly neither of these artifacts is suitable for formal reasoning about P4 in general. This paper presents a new framework, called Petr4, that puts P4 on a solid foundation. Petr4 consists of a clean-slate definitional interpreter and a core calculus that models a fragment of P4. Petr4 is not tied to any particular target: the interpreter is parameterized over an interface that collects features delegated to targets in one place, while the core calculus overapproximates target-specific behaviors using non-determinism. We have validated the interpreter against a suite of over 750 tests from the P4 reference implementation, exercising our targetmore »interface with tests for different targets. We validated the core calculus with a proof of type-preserving termination. While developing Petr4, we reported dozens of bugs in the language specification and the reference implementation, many of which have been fixed.« less

Widespread deployment of Intelligent Infrastructure and the In- ternet of Things creates vast troves of passively-generated data. These data enable new ubiquitous computing applications—such as location-based services—while posing new privacy threats. In this work, we identify challenges that arise in applying use-based privacy to passively-generated data, and we develop Ancile, a plat- form that enforces use-based privacy for applications that consume this data. We find that Ancile constitutes a functional, performant platform for deploying privacy-enhancing ubiquitous computing applications.

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.

To achieve good performance, modern applications often partition and replicate their state across multiple geographically-distributed nodes. While this approach reduces latency in the common case, it can be challenging for programmers to use correctly, especially in applications that require strong consistency. We show how to achieve strong consistency while avoiding coordination by using predictive treaties, a mechanism that can significantly reduce distributed coordination without losing strong consistency. The central insight behind our approach is that many computations can be expressed in terms of predicates over distributed state that can be partitioned and enforced locally. Predictive treaties improve on previous work by allowing the locally enforced predicates to depend on time. Intuitively, by predicting the evolution of system state, coordination can be significantly reduced compared to static approaches. We implemented predictive treaties in a distributed system that exposes them via an intuitive programming model. We evaluate performance on several benchmarks, including TPC-C, showing that predictive treaties can significantly increase performance by orders of magnitude and can even outperform customized algorithms.