  Stochastic Network Calculus is a probabilistic method to compute performance
bounds in networks, such as end-to-end delays. It relies on the analysis of
stochastic processes using formalism of (Deterministic) Network Calculus.
However, unlike the deterministic theory, the computed bounds are usually very
loose compared to the simulation. This is mainly due to the intensive use of
the Boole's inequality. On the other hand, analyses based on martingales can
achieve tight bounds, but until now, they have not been applied to sequences of
servers. In this paper, we improve the accuracy of Stochastic Network Calculus
by combining this martingale analysis with a recent Stochastic Network Calculus
results based on the Pay-Multiplexing-Only-Once property, well-known from the
Deterministic Network calculus. We exhibit a non-trivial class of networks that
can benefit from this analysis and compare our bounds with simulation.
