Petri-Nets@Run.Time : handling uncertainty during run-time adaptation using digital twins

Abstract

In this paper we study systems containing uncertainty about quantitative aspects, like delays etc. System design for such systems faces the problem that usually the system's initial configuration is chosen in a sub-optimal way due to these uncertainties. An adaptive system will adjust its configuration whenever it obtains new knowledge about the uncertain aspects. In our setting we like to derive the optimal configuration from the system's model. Therefore our models have to include a description of the uncertainties as well. To handle the change of knowledge at run-time, we integrate the model into the run-time system: models@run.time Adaptation of the configuration often requires to evaluate different variations of the model, which arise from the uncertainties. To evaluate the impact of different configurations, we simulate a digital twin of the system. Therefore, we need a formalism that allows for an easy simulation of models. To simplify the approach, we would like to use the same formalism for the simulator and the system model, i.e. we use a formalism that is able to execute itself as a sub-system. As our system model is based on Petri nets we use the reflexive approach of Hornets, which follow the Nets-within-Nets principle. In this paper, we will specify a Hornet model to handle digital twins during the planning phase of the well-known MAPE-loop (monitor-analyse-plan-execute). A simple scenario based on stochastic workflow Petri nets will serve as a proof-of-concept.