Advanced International Journal for Research

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Volume 6 Issue 6
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A Reinforcement Learning-Based Adaptive Routing Framework for Real-Time Optimization in SD-WAN Environments

Author(s) Mr. Rahul Guha, Ms. Nidhi Singh, Ms. Ambika Bagri, Mr. Pankaj Kumar Sharma
Country India
Abstract The rapid evolution of enterprise networks and the growing demand for application-specific Quality of Service (QoS) have made Software-Defined Wide Area Networks (SD-WANs) a crucial component of modern communication infrastructures. However, traditional static or rule-based routing approaches often fail to adapt efficiently to dynamic network conditions such as fluctuating link performance, congestion, and heterogeneous transport mediums. This paper proposes a Reinforcement Learning-Based Adaptive Routing Framework (RLARF) designed to optimize routing decisions in real-time for SD-WAN environments. The framework employs a deep reinforcement learning agent that continuously learns from network states, including latency, packet loss, and bandwidth utilization, to select optimal routing paths dynamically. By formulating the routing process as a Markov Decision Process (MDP), the proposed model efficiently adapts to varying link qualities and application QoS requirements while ensuring scalability across large, distributed networks. Experimental evaluations demonstrate that RLARF achieves significant improvements in throughput, latency reduction, and reliability compared to traditional routing algorithms. Furthermore, the proposed system exhibits robust performance under highly variable network conditions, making it a viable solution for next-generation, intelligent SD-WAN architectures.
Keywords Reinforcement Learning, SD-WAN Optimization, Adaptive Routing
Field Computer > Network / Security
Published In Volume 6, Issue 6, November-December 2025
Published On 2025-11-03
DOI https://doi.org/10.63363/aijfr.2025.v06i06.1817
Short DOI https://doi.org/g99qqh
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