AN AI-DRIVEN ADAPTIVE OPTIMIZATION FRAMEWORK FOR ENHANCING COMMUNICATION THROUGHPUT IN COMPUTER NETWORKS
DOI:
https://doi.org/10.15662/11he0r22Keywords:
Network Optimization, Adaptive Systems, Machine Learning, Communication Throughput, Congestion Control, Bandwidth Management, Artificial IntelligenceAbstract
Modern computer networks face unprecedented challenges in maintaining optimal throughput amid dynamic
traffic patterns, varying bandwidth demands, and unpredictable network conditions. Traditional static
optimization approaches prove inadequate for contemporary networks where conditions change rapidly and
unpredictably. This research proposes an AI-driven adaptive optimization framework that continuously monitors
network parameters and dynamically adjusts routing, bandwidth allocation, and congestion control mechanisms
to maximize communication throughput. The framework employs machine learning algorithms to predict traffic
patterns, identify bottlenecks, and implement real-time optimizations that adapt to changing network conditions.
Through comprehensive evaluation across diverse network scenarios, we demonstrate that the proposed
framework achieves substantial throughput improvements compared to conventional approaches while
maintaining stability and fairness. The research contributes both theoretical foundations for adaptive network
optimization and practical implementation strategies applicable to enterprise, data center, and telecommunications
networks. Our findings indicate that AI-driven adaptive optimization can increase average network throughput by
up to forty percent while reducing latency variability and improving overall quality of service.
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