Network Traffic Control Using AI
Keywords:
5GNetwork, Artificial Intelligence, Machine Learning, Data Clustering, Statistical Feature, Traffic Prediction, Traffic classificationAbstract
The core of next generation 5G wireless network is heterogeneous network. The existing traditional 4G technology approaches are centrally managed and reactive conception-based network which needs additional hardware for every update and when there is a demand for the resources in the network. To reduce traffic in 4G network techniques like Port based, payload and feature based approach are used. They do not provide efficient resource management and it does not classify all type of network traffic effectively. 5G helps in giving solution to the problem of 4G network using prediction and traffic learning to increase performance and bandwidth. Heterogeneous network provides more desirable Quality of Service (QOS) and explores the resources of the network explicitly. The assortment of heterogeneous network brings difficulty in traffic control of the network. The problem in heterogeneous network is network traffic which cannot be controlled and managed due to different protocols and data transfer rate. The key problem of heterogeneous network is to achieve intelligent and efficient internet traffic control. The upcoming 5G heterogeneous network cannot be fulfilled until Artificial Intelligence is deployed in the network. This work aims to explore artificial intelligence inspired network traffic scheme for reducing traffic in a heterogeneous network. In this work the proposed system consists of clustering of traffic data set, statistical feature extraction of the network data, prediction of network traffic and classification of network traffic. The clustering of network data is implemented using Enhanced Density-based spatial clustering of applications with noise (DBSCAN) algorithm. The statistical feature of the network data is extracted and they are given as input into the Modified Backpropagation model for prediction and regression tree method is used for classification of network traffic. Finally caching and pushing of network is included to make use of the network resource effectively and also to provide finer Quality of Service (QOS) in a network.
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