Application of deep neural network machine learning algorithm for end-to-end congestion control

Author

Muchenje, Nigel

Title

Application of deep neural network machine learning algorithm for end-to-end congestion control

Abstract

The system for Network Congestion Detection using Deep Neural Networks (DNNs) operates in a multi-stage process to effectively monitor and manage network traffic. Initially, the system collects real-time or historical network data, which is then pre-processed to remove noise and prepare it for analysis. In the training phase, this pre-processed data is used to train a DNN model, chosen from architectures like Convolutional Neural Networks (CNNs) or Recurrent Neural Networks (RNNs). This model is optimized iteratively using algorithms to enhance its ability to detect congestion patterns in the network

Date

June 2024

Publisher

BUSE

Keywords

Deep neural networks

Machine learning algorithms

End-to-end congestion control

Network optimization

Supervisor

Mr Matombo