In this paper, we present TauSSA, a discrete-event simulation tool for stochastic queueing networks integrated in
the LINE solver. TauSSA combines Gillespie’s stochastic
simulation algorithm with tau leaping, a methodology for
optimistic simulation acceleration. Although tau leaping is
frequently used in chemical reaction network simulation, it
has so far found limited application in queueing theory.
TauSSA offers one of the very first attempts to make
this method broadly applicable to analyze extended queueing network models, which include class switching, fork-join,
and non-exponential service and arrival distributions. We
conceptualize various strategies for handling ordering and
illegal states in tau leaping that arise specifically within
queueing network models, and compare their performance
through numerical experiments. Our main finding is that
strategies that sort events based on the network topological
order incur a better trade-off between speedup and approximation error.