A Unified Framework for Congestion Diagnosis and Dynamic Mitigation in Complex Networks
Keywords:
Network congestion, Intelligent prediction, Hierarchical cooperative controlAbstract
To address congestion in dynamic network-engineering environments, this paper proposes optimization strategies based on intelligent prediction with proactive control, a hierarchical cooperative control architecture, and multi-technology convergence. Intelligent prediction employs machine-learning models to accurately forecast traffic and couples this with proactive control to reduce congestion occurrence. The hierarchical cooperative architecture integrates edge, control, and core resources and establishes a closed-loop feedback mechanism, improving response speed. The multi-technology convergence scheme integrates AI, blockchain, and intent-driven networking to achieve cross-domain trusted collaboration and automated policy deployment. Experimental validation shows that the proposed strategies reduce network-congestion incidence by 41%, cut response time to 280 ms, and raise resource-utilization efficiency by 38%, providing a comprehensive solution for congestion management in highly dynamic network environments.
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