DRR‐MDPF: A Queue Management Strategy Based on Dynamic Resource Allocation and Markov Decision Process in Named Data Networking (NDN)

Named data networking (NDN) represents a transformative shift in network architecture, prioritizing content names over host addresses to enhance data dissemination. Efficient queue and resource management is critical to NDN performance, especially under dynamic and high‐traffic conditions. A novel hybrid strategy, DRR‐MDPF, is introduced, integrating the Markov decision process forwarding (MDPF) model with the deficit round robin (DRR) algorithm. Optimal forwarding decisions are intelligently predicted based on key metrics such as bandwidth, delay, and the number of unsatisfied Interests, whereas fair and adaptive bandwidth allocation among competing data flows is ensured by DRR. Each router is modeled as a learning agent capable of adjusting its strategies through continuous feedback and probabilistic updates. According to simulation results obtained using ndnSIM, DRR‐MDPF was found to significantly outperform state‐of‐the‐art strategies including SAF, RFA, SMDPF, and LA‐MDPF across various metrics such as throughput, interest satisfaction rate (ISR), packet drop rate, content retrieval time, and load balancing. Notably, DRR‐MDPF maintains robustness under limited cache sizes and heavy traffic, offering enhanced adaptability and lower computational complexity due to its single‐path routing design. More accurate interface selection is achieved due to the multi‐metric decision‐making capability of DRR‐MDPF, leading to optimized network performance. Overall, DRR‐MDPF serves as an intelligent, adaptive, and scalable queue management solution for NDN, effectively addressing core challenges such as resource allocation, congestion control, and route optimization in dynamic networking environments.