Traffic Flows Forecasting Based on Machine Learning

The article aims to develop a model for forecasting the characteristics of traffic flows in real-time based on the classification of applications using machine learning methods to ensure the quality of service. It is shown that the model can forecast the mean rate and frequency of packet arrival for...

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Bibliographic Details
Published inInternational journal of embedded and real-time communication systems Vol. 13; no. 1; pp. 1 - 19
Main Authors Deart, Vladimir, Mankov, Vladimir, Krasnova, Irina
Format Journal Article
LanguageEnglish
Published Hershey IGI Global 01.01.2022
Subjects
Forecasting
Machine learning
Mathematical models
Packet switching
Traffic flow
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ISSN1947-3176
1947-3184
DOI10.4018/IJERTCS.289198

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Summary:The article aims to develop a model for forecasting the characteristics of traffic flows in real-time based on the classification of applications using machine learning methods to ensure the quality of service. It is shown that the model can forecast the mean rate and frequency of packet arrival for the entire flow of each class separately. The prediction is based on information about the previous flows of this class and the first 15 packets of the active flow. Thus, the Random Forest Regression method reduces the prediction error by approximately 1.5 times compared to the standard mean estimate for transmitted packets issued at the switch interface.
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ISSN:1947-3176
1947-3184
DOI:10.4018/IJERTCS.289198