Proposing an AI-based model to manage and control congestion at routers on the internet

Abstract

The rapid growth of multimedia communication applications drives the demand for network communication, creating significant pressure on network systems and requiring effective queue management solutions to maintain performance and minimize congestion. With fluctuating traffic loads and increasing demands for Quality of Service, traditional queue management methods fail to meet the requirements. To resolve the challenge, the paper proposes the enhanced DAIM-RED model. DAIM-RED offers a comprehensive solution, leveraging adaptive techniques to adjust min-max thresholds, reduce packet drop probability, and optimize queue management efficiency in network routers. The model incorporates AIM-RED, an adaptive method capable of automatically updating the model and tuning parameters based on network data, along with Deep Q-Network, which predicts queue overflow conditions and optimizes throughput. DAIM-RED demonstrates superior network performance compared to models combining AIM-RED with Convolutional Neural Networks and Long Short-Term Memory. The model not only stabilizes queues but also minimizes congestion and ensures Quality of Service in increasingly complex network environments.