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

Stochastic Network Calculus with Localized Application of Martingales