AI-Powered Network Traffic Prediction

Abstract: In this Internet and big data era, resource management has become a crucial task to ensure the quality of service for users in modern wireless networks. Accurate and rapid Internet traffic data is essential for many applications in computer networking to enable high networking performance. Such applications facilitate admission control, congestion control, anomaly detection, and bandwidth allocation. In radio networks, these mechanisms are typically handled by features such as Carrier Aggregation, Inter-Frequency Handover, and Predictive Scheduling. Since these mechanisms often take time and cost radio resources, it is desirable to only enable them for users expected to gain from them. The problem of network traffic flow prediction is forecasting aspects of an ongoing traffic flow to mobilize networking mechanisms that ensures both user experience quality and resource management. The expected size of an active traffic flow, its expected duration, and the anticipated amount of packets within the flow are some of the aspects. Additionally, forecasting individual packet sizes and arrival times can also be beneficial. The wide-spread availability of Internet flow data allows machine learning algorithms to learn the complex relationships in network traffic and form models capable of forecasting traffic flows. This study proposes a deep-learning-based flow prediction method, established using a residual neural network (ResNet) for regression. The proposed model architecture demonstrates the ability to accurately predict the packet count, size, and duration of flows using only the information available at the arrival of the first packet. Additionally, the proposed method manages to outperform traditional machine learning methods such as linear regression and decision trees, in addition to conventional deep neural networks. The results indicate that the proposed method is able to predict the general magnitude of flows with high accuracy, providing precise magnitude classifications.