Consensus Refined

Algorithms for solving the consensus problem are fundamental to distributed computing. Despite their brevity, their ability to operate in concurrent, asynchronous, and failure-prone environments comes at the cost of complex and subtle behaviors. Accordingly, understanding how they work and proving their correctness is a non-trivial endeavor where abstraction is immensely helpful. Moreover, research on consensus has yielded a large number of algorithms, many of which appear to share common algorithmic ideas. A natural question is whether and how these similarities can be distilled and described in a precise, unified way. In this work, we combine stepwise refinement and lockstep models to provide an abstract and unified view of a sizeable family of consensus algorithms. Our models provide insights into the design choices underlying the different algorithms, and classify them based on those choices. All our results are formalized and verified in the theorem prover Isabelle/HOL, yielding precision and strong correctness guarantees.