Big Data Analytics for Network Traffic Prediction and Performance Optimization

Abstract

The exponential growth of Internet services, cloud computing, mobile communications, and Internet of Things (IoT) applications has resulted in unprecedented increases in network traffic volume, velocity, and variety. Modern communication networks continuously generate massive amounts of heterogeneous traffic data that must be analyzed in real time to ensure efficient resource utilization, congestion control, Quality of Service (QoS), and network reliability. Traditional traffic analysis techniques often struggle to process high-dimensional network data due to computational limitations, making accurate traffic prediction and dynamic performance optimization increasingly challenging. Consequently, Big Data analytics has emerged as an effective solution for processing large-scale network traffic datasets and enabling intelligent network management through predictive analytics. Between 2008 and 2018, significant advances were achieved in distributed computing, Hadoop ecosystems, MapReduce processing, Apache Spark, stream analytics, statistical forecasting, and early machine learning techniques for network traffic prediction. These technologies enabled scalable processing of large traffic datasets while supporting predictive decision-making for bandwidth allocation, congestion avoidance, load balancing, anomaly detection, and network resource optimization. The integration of Big Data platforms with predictive analytics significantly improved the capability of network administrators to anticipate traffic fluctuations and optimize network performance proactively.