Ai-based Automatic Network Traffic Routing To Avoid Congestion

Abstract

The Exponential Increase In Network Traffic Has Made Congestion A Major Challenge, Leading To Increased Latency, Packet Loss, And Reduced Quality Of Service (QoS). This Paper Presents An Intelligent And Adaptive Traffic Routing System That Integrates Software-Defined Networking (SDN), Python-based Automation, And Artificial Intelligence/Machine Learning (AI/ML) To Proactively Detect And Mitigate Network Congestion. The Proposed System Utilizes The Centralized Control Capability Of SDN To Continuously Monitor Network Conditions And Dynamically Manage Traffic Flows. Machine Learning Algorithms Are Trained Using Both Historical And Real-time Network Data To Accurately Predict Congestion Based On Key Performance Metrics Such As Bandwidth Utilization, Delay, And Packet Loss. Upon Identifying Potential Congestion, The System Automatically Selects Optimal Alternative Paths And Reroutes Traffic In Real Time, Ensuring Efficient Bandwidth Utilization And Reduced Network Delays. Experimental Evaluation Shows That The Integration Of AI/ML With SDN Significantly Improves Throughput, Reduces Congestion Levels, And Achieves Effective Load Balancing. This Research Demonstrates A Smart And Scalable Approach For Next-generation Networks, Suitable For Data Centers, Cloud Computing, And IoT Infrastructures.

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