Analyzing stochastic reward nets by model checking and parallel simulation

This paper deals with Stochastic Reward Nets (SRN), which are a powerful extension of Generalized Stochastic Petri Nets (GSPN). SRN have proved their usefulness in modelling and analysis of performance, availability and reliability of complex timed systems. SRN are supported by special-case tools like the Stochastic Petri Net Package (SPNP) which enables both analytic (based on Markov Reward Models) and simulative studies. The work described in this paper argues that there is still a gap in SRN analysis concerning functional correctness and non-deterministic property checking. Toward this a novel approach is proposed, which is based on two developed tools. First a formal reduction of SRN onto the Timed Automata (TA) of the popular Uppaal toolbox was defined and implemented. The Uppaal reduction enables a more complete investigation of SRN models, not allowed by existing SRN tools. However, the practical use of Uppaal forbids to study the performance of large models. Then, an SRN kernel, inspired by the carried formal Uppaal modelling and reasoning, was achieved in Java using the Theatre actor system. The realization supports the parallel simulation of scalable models. The paper applies the developed tools to a realistic grid-computing model and reports some experimental results, together with good execution performance (speedup) when using a scalable version of the grid model on a shared memory multi-core machine.