NETWORK TRAFFIC PREDICTION: USING AI TO PREDICT AND MANAGE TRAFFIC IN HIGH-DEMAND IT NETWORKS

Syed Muhammad Shakir Bukhari; Taimoor Ali Khan; Muhammad Ahmad Siddiqui; Umer Mustafa; Waseema Batool; Ibrahim Lughmani

Authors

Syed Muhammad Shakir Bukhari
Taimoor Ali Khan
Muhammad Ahmad Siddiqui
Umer Mustafa
Waseema Batool
Ibrahim Lughmani

Keywords:

Network Traffic Prediction, Artificial Intelligence, Machine Learning, Deep Learning, High-Demand Networks, Load Balancing, Congestion Management, Network Optimization, Reinforcement Learning

Abstract

The rapid growth of internet traffic due to digital transformation, IoT, and cloud computing has led to increased complexity in managing network resources. Network traffic prediction is crucial for optimizing network performance, especially in highdemand
IT networks that require real-time decision-making. This paper explores the application of Artificial Intelligence (AI) techniques in predicting network traffic patterns and effectively managing congestion, load balancing, and resource allocation. We discuss machine learning (ML) algorithms, deep learning (DL) models, and hybrid AI techniques that have been developed to forecast traffic in high-demand networks. We also analyze recent advancements in AI for traffic prediction, including reinforcement learning and neural networks, while evaluating their effectiveness in different network environments. The paper concludes with the future potential of AI in enabling autonomous network management systems capable of self-healing and optimization.