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Markov models of internet traffic and a new hierarchical MMPP model

Authors:

L. Muscariello,

M. Ajmone Marsan,

R. Lo CignoAuthors Info & Claims

Computer Communications, Volume 28, Issue 16

Pages 1835 - 1851

https://doi.org/10.1016/j.comcom.2005.02.012

Published: 01 October 2005 Publication History

Abstract

The first part of this paper gives a short tutorial survey of Internet traffic modeling, focusing on recent advances in Markov models showing pseudo-LRD (Long Range Dependence) characteristics that match those measured on the Internet. The interest in Markov models of Internet traffic, in spite of the impossibility to achieve true LRD or Self-Similarity, lies in the possibility of exploiting powerful analytical techniques to predict the network performance, which is the ultimate goal when adopting models to either study existing networks or design new ones. Then, the paper describes a new MMPP (Markov Modulated Poisson Process) traffic model that accurately approximates the LRD characteristics of Internet traffic traces over the relevant time scales. The heart of the model is based on the notion of sessions and flows, trying to mimic the real hierarchical generation of packets in the Internet. The proposed model is simple and intuitive: its parameters have a physical meaning, and the model can be tuned with only a few input parameters. Results prove that the queuing behavior of the traffic generated by the MMPP model is coherent with the one produced by real traces collected at our institution edge router under several different traffic loads. Due to its characteristics, the proposed MMPP traffic model can be used as a simple and manageable tool for IP network dimensioning, design and planning: the paper provides examples of its application in both simulative and theoretical analysis.

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Aceto GBovenzi GCiuonzo DMontieri APersico VPescapé A(2021)Characterization and Prediction of Mobile-App Traffic Using Markov ModelingIEEE Transactions on Network and Service Management10.1109/TNSM.2021.305138118:1(907-925)Online publication date: 1-Mar-2021
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Lee AWang PLin SAkyildiz ILuo MHung CSaid L(2018)Dynamic bandwidth allocation in SDN based next generation virtual networksProceedings of the 2018 Conference on Research in Adaptive and Convergent Systems10.1145/3264746.3264754(80-87)Online publication date: 9-Oct-2018
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Nevat IDivakaran DNagarajan SZhang PSu LKo LThing V(2018)Anomaly Detection and Attribution in Networks With Temporally Correlated TrafficIEEE/ACM Transactions on Networking10.1109/TNET.2017.276571926:1(131-144)Online publication date: 1-Feb-2018
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Markov models of internet traffic and a new hierarchical MMPP model
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Published In

cover image Computer Communications

Computer Communications Volume 28, Issue 16

October, 2005

89 pages

ISSN:0140-3664

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Copyright © Elsevier B.V. © 2005.

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Elsevier Science Publishers B. V.

Netherlands

Publication History

Published: 01 October 2005

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Cited By

Aceto GBovenzi GCiuonzo DMontieri APersico VPescapé A(2021)Characterization and Prediction of Mobile-App Traffic Using Markov ModelingIEEE Transactions on Network and Service Management10.1109/TNSM.2021.305138118:1(907-925)Online publication date: 1-Mar-2021
https://dl.acm.org/doi/10.1109/TNSM.2021.3051381
Lee AWang PLin SAkyildiz ILuo MHung CSaid L(2018)Dynamic bandwidth allocation in SDN based next generation virtual networksProceedings of the 2018 Conference on Research in Adaptive and Convergent Systems10.1145/3264746.3264754(80-87)Online publication date: 9-Oct-2018
https://dl.acm.org/doi/10.1145/3264746.3264754
Nevat IDivakaran DNagarajan SZhang PSu LKo LThing V(2018)Anomaly Detection and Attribution in Networks With Temporally Correlated TrafficIEEE/ACM Transactions on Networking10.1109/TNET.2017.276571926:1(131-144)Online publication date: 1-Feb-2018
https://dl.acm.org/doi/10.1109/TNET.2017.2765719
Dong FWu KSrinivasan V(2017)Copula Analysis of Temporal Dependence Structure in Markov Modulated Poisson Process and Its ApplicationsACM Transactions on Modeling and Performance Evaluation of Computing Systems10.1145/30892542:3(1-28)Online publication date: 29-Jun-2017
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