Providing end-to-end statistical performance guarantees with bounding interval dependent stochastic models

Abstract

This paper demonstrates a new, efficient, and general approach for providing end-to-end performance guarantees in integrated services networks. This is achieved by modeling a traffic source with a family of bounding interval-dependent (BIND) random variables and by using a rate-controlled service discipline inside the network. The traffic model stochastically bounds the number of bits sent over time intervals of different length. The model captures different source behavior over different time scales by making the bounding distribution an explicit function of the interval length. The service discipline, RCSP, has the priority queueing mechanisms necessary to provide performance guarantees in integrated services networks. In addition, RCSP provides the means for efficiently extending the results from a single switch to a network of arbitrary topology. These techniques are derived analytically and then demonstrated with numerical examples.