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Rare properties remain a challenge for statistical model checking (SMC) dueto the quadratic scaling of variance with rarity. We address this with avariance reduction framework based on lightweight importance splittingobservers. These expose the model-property automaton to allow the constructionof score functions for high performance algorithms.  The confidence intervals defined for importance splitting make it appealingfor SMC, but optimising its performance in the standard way makes distributioninefficient. We show how it is possible to achieve equivalently good results inless time by distributing simpler algorithms. We first explore the challengesposed by importance splitting and present an algorithm optimised fordistribution. We then define a specific bounded time logic that is compiledinto memory-efficient observers to monitor executions. Finally, we demonstrateour framework on a number of challenging case studies.

Distributed Verification of Rare Properties using Importance Splitting Observers