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The rapid evolution of modern network traffic, characterized by its dynamic and unpredictable nature, poses significant challenges for traditional static routing protocols. While Software-Defined Networking (SDN) and programmable data planes like P4 have emerged as promising solutions to enhance network flexibility, a key challenge remains in developing intelligent routing decision-making mechanisms that can adapt in real-time to changing network conditions. This paper proposes a novel intelligent routing framework that integrates the Q-Learning algorithm with a P4-based programmable data plane. By modeling the routing problem as a reinforcement learning task, our approach enables network agents to autonomously learn optimal routing policies based on real-time network states, such as congestion and latency, without the need for pre-defined rules. We define the network state, agent actions, and a reward function to guide the learning process. The learned optimal routing policies are then dynamically translated into P4 rules and deployed on the data plane. Through extensive simulations, we demonstrate that our proposed Q-Learning-based algorithm significantly outperforms traditional routing protocols in terms of reducing end-to-end latency and improving network throughput.

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Data Plane Driven Adaptive Routing with In-Network Reinforcement Learning

  • 会议论文

  • 发表日期：

    2026年

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  • Bo Wu,
  • Meiju Yu,
  • Pantong Wang,
  • Dan Qin,
  • Xiliang Pang,
  • Guiquan Zheng

摘要

The rapid evolution of modern network traffic, characterized by its dynamic and unpredictable nature, poses significant challenges for traditional static routing protocols. While Software-Defined Networking (SDN) and programmable data planes like P4 have emerged as promising solutions to enhance network flexibility, a key challenge remains in developing intelligent routing decision-making mechanisms that can adapt in real-time to changing network conditions. This paper proposes a novel intelligent routing framework that integrates the Q-Learning algorithm with a P4-based programmable data plane. By modeling the routing problem as a reinforcement learning task, our approach enables network agents to autonomously learn optimal routing policies based on real-time network states, such as congestion and latency, without the need for pre-defined rules. We define the network state, agent actions, and a reward function to guide the learning process. The learned optimal routing policies are then dynamically translated into P4 rules and deployed on the data plane. Through extensive simulations, we demonstrate that our proposed Q-Learning-based algorithm significantly outperforms traditional routing protocols in terms of reducing end-to-end latency and improving network throughput.