Incremental Deep Learning Application for Network Traffic Management

One of the characteristics of 5G networks is their ability to create multiple virtual networks across a shared infrastructure, with resources dynamically allocated to meet the needs of different applications. However, managing these partitions in real-time remains a challenge due to their dynamic and heterogeneous nature. To overcome this challenge, this paper proposes an incremental learning model (ILM) that gradually learns from changing network data, seeking to improve the accuracy of partition selection. Unlike traditional models trained from static datasets, the ILM continuously updates its knowledge without the need to retrain from scratch. The results demonstrate the effectiveness of the proposed method, with an accuracy rate of 94.7%, while also demonstrating the ability to train using new traffic patterns and environments.