<b>Enhancing Bandwidth Allocation in ISPs Through Real-Time BitTorrent Traffic Prediction with Adaptive Kalman Filtering</b>

The exponential growth of peer-to-peer (P2P) traffic, particularly BitTorrent, poses significant challengesfor Internet Service Providers (ISPs) in managing bandwidth efficiently. Traditional bandwidth allocationmethods often struggle with unpredictable P2P traffic fluctuations, leading to network congestion andsuboptimal resource utilization. This study proposes an advanced bandwidth management framework thatleverages real-time BitTorrent traffic prediction using Adaptive Kalman Filtering (AKF). The AKF modeldynamically adjusts its parameters to track BitTorrent traffic variations accurately, enabling ISPs toproactively allocate bandwidth based on anticipated demand. By integrating this predictive model with anadaptive bandwidth allocation strategy, ISPs can optimize network performance, reduce latency, andenhance user experience. Experimental evaluations demonstrate that our approach outperformsconventional static allocation methods by improving prediction accuracy, minimizing bandwidth wastage,and ensuring fair resource distribution. The findings suggest that real-time traffic prediction using AKFcan serve as a robust tool for ISPs in handling P2P network traffic more effectively.