2009Unpublished venueRequires access

Traffic generator model for an n-node network

Janusz Kolbusz, Janusz Korniak, S. Paszczynski

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Abstract

In the paper a model of the traffic in the LAN is presented. In the model the most important components influencing the network traffic are taken into account. Namely, the transmission protocols and information buffering, operational systems and queuing algorithms as well as users' behavior working with the network applications are considered. The model is based on an “on-off” function. It has been shown that self-similarity of traffic measured by the Hurst parameter changes from almost 1.0 for very low frequencies to 0.5 for high frequencies. Generated and real traffic in a LAN network were compared. Implications, which are derived from the model, are described in the conclusion.

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What this paper is about

In the paper a model of the traffic in the LAN is presented. In the model the most important components influencing the network traffic are taken into account. Namely, the transmission protocols and information buffering, operational systems and queuing algorithms as well as users' behavior working with the network applications are considered. The model is based on an “on-off” function. It has been shown that self-similarity of traffic measured by the Hurst parameter changes from almost 1.0 for very low frequencies to 0.5 for high frequencies. Generated and real traffic in a LAN network were compared. Implications, which are derived from the model, are described in the conclusion.

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Available abstract

In the paper a model of the traffic in the LAN is presented. In the model the most important components influencing the network traffic are taken into account. Namely, the transmission protocols and information buffering, operational systems and queuing algorithms as well as users' behavior working with the network applications are considered. The model is based on an “on-off” function. It has been shown that self-similarity of traffic measured by the Hurst parameter changes from almost 1.0 for very low frequencies to 0.5 for high frequencies. Generated and real traffic in a LAN network were compared. Implications, which are derived from the model, are described in the conclusion.

Key concepts: Computer science, Generator (circuit theory), Node (physics), Computer network, Traffic generation model, Engineering, Power (physics), Physics

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