Loss behavior of internet router with priority based self-similar synchronous traffic-multi server queueing system with Markovian input

Gudimalla, Ravi Kumar; Perati, Malla Reddy

doi:10.1007/s12597-016-0284-3

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Markov Chains and The Markov Traffic Sources1

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“…Due to the potentially very large sizes of the models' transition matrices, their generation is parallelized, and they can be compressed in real-time using a dedicated compression method based on a finite-state automata. Markov based models are currently applied to analyze and evaluate self-similar network traffic [30][31][32][33][34]. We are currently able to generate and solve Markov chains with 150 million states.…”

Section: Markov Chains and The Markov Traffic Sourcesmentioning

confidence: 99%

Self-Similar Markovian Sources

et al. 2020

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Markov queueing models are a powerful tool to evaluate the performance of computer networks and have been used in telecommunication studies for over 100 years. To apply them to the evaluation of the modern Internet, we should not only adapt them to the contemporary network structures but also include a description of the complex stochastic patterns (self-similarity and long-range dependance) of transmitted flows. We examine the features of two Markov models of an almost self-similar process, keeping in mind the modeling of Internet traffic. We have found that the obtained results are comparable with those achieved using a well-known generator of self-similar traffic.

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