On the ratio of communications to computation in DCR efficiency metrics

We propose a way to define the most expensive operation to be used in evaluations of complexity and efficiency for simulated distributed constraint reasoning (DCR) algorithms. We also report experiments showing that the cost associated with a constraint check, even within the same algorithm, depends on the problem size. The DCR research has seen heated debate regarding the correct way to evaluate efficiency of simulated algorithms. DCR has to accommodate two established practices coming from very different fields: distributed computing and constraint reasoning. The efficiency of distributed algorithms is typically evaluated in terms of the network load and overall computation time, while many (synchronous) algorithms are evaluated in terms of the number of rounds that they require. Constraint reasoning research evaluates efficiency in terms of constraint checks and visited search-tree nodes. We argue that an algorithm has to be evaluated from the point of view of specific operating points, namely of possible or targeted application scenarios. We then show how to report efficiency for a given operating point based on simulation. Additionally, new experiments we report here show the fact that the cost of a constraint check varies with the size of the problem, and we discuss the implications of this phenomenon.