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Comparison of steady-state methods computing Markov modulated fluid models

The Markov-modulated fluid models (MMFM) are widely used in networks modeling. They assimilate the network traffic to a fluid generated by sources at a rate controlled by a background Markov process. Bursts of data are considered as continuous fluid flows and variations of cell inter-arrival times are assumed too small compared with the burst times for traffic evolving periodically in different timescales.