Quantitative Program Logic and Performance in Probabilistic Distributed Algorithms

In this paper we show how quantitative program logic [14] provides a formal framework in which to promote standard techniques of program analysis to a context where probability and nondeterminism interact, a situation common to probabilistic distributed algorithms. We show that overall performance can be formulated directly in the logic and that it can be derived from local properties of components. We illustrate the methods with an analysis of performance of the probabilistic dining philosophers [10].