Process models are an abstraction used in several domains such as manufacturing (transformation chains), logistics (procurement and distribution networks), architecture of electronics systems (network of data/computation nodes). Such systems are often subject to requirements related to the proces-sing delay, throughput, overall reliability, or quality attributes of specific outputs. Those characteristics are highly dynamic. Assessing them at design time requires some kind of execution of the model, typically using simulation. As the system is often non-deterministic, several simulations need to be run and combined in order to draw relevant conclusions.
In this paper, we describe a tool, called SimQRi, that we developed to efficiently run a large number of simulations over process models, using Discrete Event Simulation combined with Monte-Carlo techniques. Their key point is that the properties to be assessed are formulated as queries over the model with a trace semantics. Queries are evaluated and aggregated through simulations, so there is no need to store traces and perform post-processing on them. Several operators are available on different process-related components (storage content, process activity, number of processes items, etc). In our demo we will demonstrate how the tool can be used 1) to assess several risks on supply chains and 2) to design a green Cloud to cope with response times with optimal energy usage.