Open AccessTraffic optimization applying machine learning methods
Author(s) -
Daria Alekseeva,
A. Marochkina,
Alexander Paramonov
Publication year - 2021
Publication title -
informacionnye tehnologii i telekommunikacii
Language(s) - English
Resource type - Journals
ISSN - 2307-1303
DOI - 10.31854/2307-1303-2021-9-1-1-12
Subject(s) - boosting (machine learning) , gradient boosting , adaboost , computer science , artificial intelligence , machine learning , random forest , big data , ensemble learning , data mining , support vector machine
Future networks bring higher communication requirements in latency, computations, data quality, etc. The attention to various challenges in the network field through the advances of Artificial Intelligence (AI), Machine Learning (ML) and Big Data analysis is growing. The subject of research in this paper is 4G mobile traffic collected during one year. The amount of data retrieved from devices and network management are motivating the trend toward learning-based approaches. The research method is to compare various ML methods for traffic prediction. In terms of ML, to find a solution for a regression problem using the ensemble models Random Forest, Boosting, Gradient Boosting, and Adaptive Boosting (AdaBoost). The comparison was based on the quality indicators RMSE, MAE, and coefficient of determi-nation. In the result Gradient Boosting showed the most accurate prediction. Using this ML model for mobile traffic optimization could improve network performance.