Performance Model Checking Scenario-Aware Dataflow

Dataflow formalisms are useful for specifying signal processing and streaming applications. To adequately capture the dynamic aspects of modern applications, the formalism of Scenario-Aware Dataflow (SADF) was recently introduced, which allows analysis of worst/best-case and average-case performance across different modes of operation (scenarios). The semantic model of SADF integrates non-deterministic and discrete probabilistic behaviour with generic discrete time distributions. This combination is different from the semantic models underlying contemporary quantitative model checking approaches, which often assume exponentially distributed or continuous time or they lack support for expressing discrete probabilistic behaviour. This paper discusses a model-checking approach for computing quantitative properties of SADF models such as throughput, time-weighted average buffer occupancy and maximum response time. A compositional state-space reduction technique is introduced as well as an efficient implementation of this method that combines model construction with on-the-fly state-space reductions. Strong reductions are possible because of special semantic properties of SADF, which are common to dataflow models. We illustrate this efficiency with several case studies from the multi-media domain.