Research of productivity of software configurable infrastructure in vanet networks on the basis of models of hybrid data transmission devices

M Ushakova, Y Ushakov, I Bolodurina… - 2020 International …, 2020 - ieeexplore.ieee.org
M Ushakova, Y Ushakov, I Bolodurina, D Parfenov, L Legashev, A Shukhman
2020 International Scientific and Technical Conference Modern …, 2020ieeexplore.ieee.org
Development of new generation networks based on software-defied networks and their
inclusion in the 5G stack requires new approaches to studying the operation of such
networks. Most researchers trust frameworks for all low-level work, focusing on higher-level
metrics. However, most modeling tools have a very limited learning curve for software-defied
hardware, especially packet processing latency. Also, not enough attention has been paid to
the models of virtual network devices, which behave differently from physical hardware and …
Development of new generation networks based on software-defied networks and their inclusion in the 5G stack requires new approaches to studying the operation of such networks. Most researchers trust frameworks for all low-level work, focusing on higher-level metrics. However, most modeling tools have a very limited learning curve for software-defied hardware, especially packet processing latency. Also, not enough attention has been paid to the models of virtual network devices, which behave differently from physical hardware and have different performance parameters and dependence on external factors. All this led to the writing of this article, the purpose of which is to study the internal structure of network equipment models of the OmNET ++ modeling system, as well as create alternative models that take into account all the features of various software-defined equipment implementations. As a result of the study of the created models, an improvement in the simulation accuracy in terms of packet processing delay parameters is shown compared to the traditionally used building blocks of network equipment models.
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