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Network calculus is an elegant theory which uses envelopes to determine theworst-case performance bounds in a network. Statistical network calculus is theprobabilistic version of network calculus, which strives to retain thesimplicity of envelope approach from network calculus and use the arguments ofstatistical multiplexing to determine probabilistic performance bounds in anetwork. The tightness of the determined probabilistic bounds depends on theefficiency of modelling stochastic properties of the arrival traffic and theservice available to the traffic at a network node. The notion of effectivebandwidth from large deviations theory is a well known statistical descriptorof arrival traffic. Similarly, the notion of effective capacity summarizes thetime varying resource availability to the arrival traffic at a network node.The main contribution of this paper is to establish an end-to-end stochasticnetwork calculus with the notions of effective bandwidth and effective capacitywhich provides efficient end-to-end delay and backlog bounds that growslinearly in the number of nodes ($H$) traversed by the arrival traffic, underthe assumption of independence.

An End-to-End Stochastic Network Calculus with Effective Bandwidth and Effective Capacity