Ultra-Fast Throughput Estimation based on Intelligent Network Sampling

The exponential growth of network traffic in modern telecommunications has made the task of analyzing flow data for effective bandwidth estimation increasingly complex and resource-intensive. Accurate estimation of effective throughput is essential for a wide range of network management tasks, including dynamic traffic shaping, congestion control, quality optimization, and anomaly detection. However, traditional packet-based solutions, which rely on inspecting packets across the entire flow duration, demand substantial computational resources and memory, thereby reducing system performance and limiting scalability under high traffic volumes. This paper introduces an efficient sampling method based on linear regression and error reduction to accurately estimate effective throughput. Unlike general sampling methods such as random or systematic sampling, which do not account for actual network conditions or flow dynamics, our approach leverages real-time flow behavior to guide the sampling process. By focusing only on the most informative and impactful portions of each flow, the method significantly reduces the amount of data that needs to be processed while maintaining high estimation accuracy, allowing accurate throughput estimation using less than 15% of the available data, with an estimation error of no more than 10%. These advantages make the proposed method highly suitable for scalable and efficient deployment in diverse real-time network monitoring and traffic analysis environments.