Providing End-to-End Statistical Delay Guarantees with Non-Gaussian Input Traffic

The statistical approaches of resource allocation require that input traffic is Gaussian. The Gaussian hypothesis is guaranteed by the central limit theorem (CLT), that is verified when several hundreds of independent flows are multiplexed. In realistic scenarios few flows and long range dependent video traces can be multiplexed, in this case the input traffic can´t be considered Gaussian. In this paper, we propose a procedure in order to evaluate the statistical quality-of-service (QoS) guarantees expressed in terms of a delay bound, d and a delay violation probability, p. In particular, we extend the network calculus, developed for Gaussian traffic and founded on the maximum variance approximation (MVA), to scenarios with non-Gaussian traffic where few users download MPEG movies. The analysis is carried out for single and multiple-node, with homogeneous and heterogeneous traffic flows. We provide an approximation of the end-to-end delay violation probability evaluated by means of a new framework for stochastic network calculus, denoted as the bounded-variance network calculus. The results show that our procedure, in all scenarios, provides a delay violation probability for non-Gaussian input traffic (MPEG video traces), very close to the values obtained with simulations.