Imperial College London
Stochastic performance models provide a powerful way of capturing and analyzing the behavior of complex concurrent systems. Traditionally, performance measures for these models are derived by generating and then analyzing a (semi-)Markov chain corresponding to the model's behavior at the state-transition level. However, and especially when analyzing industrial-scale systems, workstation memory and compute power is often overwhelmed by the sheer number of states. This chapter explores an array of techniques for analyzing stochastic performance models with large state spaces.