  The stochastic network calculus (SNC) holds promise as a framework to
calculate probabilistic performance bounds in networks of queues. A great
challenge to accurate bounds and efficient calculations are stochastic
dependencies between flows due to resource sharing inside the network. However,
by carefully utilizing the basic SNC concepts in the network analysis the
necessity of taking these dependencies into account can be minimized. To that
end, we fully unleash the power of the pay multiplexing only once principle
(PMOO, known from the deterministic network calculus) in the SNC analysis. We
choose an analytic combinatorics presentation of the results in order to ease
complex calculations. In tree-reducible networks, a subclass of a general
feedforward networks, we obtain a perfect analysis in terms of avoiding the
need to take internal flow dependencies into account. In a comprehensive
numerical evaluation, we demonstrate how this unleashed PMOO analysis can
reduce the known gap between simulations and SNC calculations significantly,
and how it favourably compares to state-of-the art SNC calculations in terms of
accuracy and computational effort. Driven by these promising results, we also
consider general feedforward networks, when some flow dependencies have to be
taken into account. To that end, the unleashed PMOO analysis is extended to the
partially dependent case and a case study of a canonical example topology,
known as the diamond network, is provided, again displaying favourable results
over the state of the art.
