Closed-form analysis of end-to-end network delay with Markov-modulated Poisson and fluid traffic

This paper develops a method for using traffic sources modelled as a Markov-modulated Poisson process (MMPP) and Markov-modulated fluid process (MMFP) in the framework of the bounded-variance network calculus, a novel stochastic network calculus framework for the approximated analysis of end-to-end network delay. The bounded-variance network calculus is an extension to multi-hop end-to-end paths of the Choe's and Shroff's Central-Limit-Theorem-based analysis of isolated network nodes. The input of the analysis is the statistical traffic envelope of sources, which is not available for generic MMPP and MMFP sources. The paper provides two statistical traffic envelopes, named two-moment and linear envelope, for general MMPP and MMFP sources, which can be used as an input of Central-Limit-Theorem-based frameworks for the analysis of network delay and, in turn, make it possible to use the rich collection of MMPP and MMFP models of voice, audio, data and video sources available in the literature. In this way, it is possible to avoid the computational complexity of traditional Markov analysis of end-to-end delay with MMPP and MMFP sources. With the linear envelope we can use simple analytical closed-form solutions for many important schedulers.

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