Derivation of Fault Tolerance Measures of Self-Stabilizing Algorithms by Simulation

Fault tolerance measures can be used to distinguish between different self-stabilizing solutions to the same problem. However, derivation of these measures via analysis suffers from limitations with respect to scalability of and applicability to a wide class of self-stabilizing distributed algorithms. We describe a simulation framework to derive fault tolerance measures for self-stabilizing algorithms which can deal with the complete class of self-stabilizing algorithms. We show the advantages of the simulation framework in contrast to the analytical approach not only by means of accuracy of results, range of applicable scenarios and performance, but also for investigation of the influence of schedulers on a meta level and the possibility to simulate large scale systems featuring dynamic fault probabilities.