SIMULATION OF LARGE-SCALE NETWORKS USING SSF

Some applications of simulation require that the model state be advanced in simulation time faster than the wall-clock time advances as the simulation executes. This faster than real-time requirement is crucial, for instance, when a simulation is used as part of a real-time control system, working through the consequences of contemplated control actions, in order to identify feasible (or even optimal) decisions. This paper con-siders the issue of faster than real-time simulation of very large communication networks, and how this is ac-complished using our implementation (in C++) of the Scalable Simulation Framework (SSF). Our tool (called iSSF) uses hierarchical levels of abstraction, and paral-lelism, to achieve speedups of nearly four orders of magnitude, enabling real-time execution rates on large network models. We quantify the effects that choice of hierarchical abstraction has on the simulation time advance rate, and show empirically how changing the abstraction mix affects the execution rate on a large network example.