Delay-driven Maximum Network Utilization by Machine Learning Model for Congestion Control

The proposed work maintains autonomous control, enabling fair and effective bandwidth distribution without the requirement of router-specific link-layer information. Five ML models—Decision Tree, k-NN, Naive Bayes, Fuzzy Logic, and SVM—are evaluated to guide the delay-based congestion adjustments, with SVM achieving the highest accuracy and lowest delay. Simulation results demonstrate that DDCCP significantly improves bandwidth utilization, reduces packet loss, accelerates fairness convergence, and adapts efficiently to caching effects in multipath CCN environments. The proposed framework offers a scalable, intelligent, and high-performance alternative to current CCN congestion control strategies.