Toward Self-Driving Networks: A Reinforcement Learning Framework for Autonomous Traffic Engineering in SDN-Based Architectures

Engineering notes

Experimental evaluation on Mininet-based SDN emulation environments with synthetic and real-world Internet2 and G?ANT topology traffic traces demonstrates that RL-ATE achieves up to 38.7% improvement in network throughput, 42.3% reduction in end-to-end latency, and 31.5% improvement in link utilization efficiency compared to OSPF, ECMP, and heuristic SDN-TE baselines.

Chinese explanation / 中文解读

中文解读待补充：本站会优先为端到端自动驾驶、BEV感知、3D目标检测、轨迹预测、路径规划、LiDAR感知等高价值论文补充中文说明。

Original abstract

The exponential proliferation of heterogeneous network traffic, coupled with the increasingly stringent quality-of-service (QoS) requirements of modern applications including real-time video streaming, cloud-native microservices, Internet of Things (IoT) telemetry, and latency-critical industrial automation has rendered traditional static traffic engineering (TE) approaches fundamentally inadequate for next-generation network management. Software-Defined Networking (SDN) has emerged as a transformative paradigm that decouples the control plane from the data plane, enabling programmable, centralized network management with global topology awareness. However, the dynamic, non-stationary, and stochastic nature of contemporary traffic demands an intelligence layer beyond conventional SDN-based TE heuristics. This paper presents a comprehensive reinforcement learning (RL) framework termed RL-ATE (Reinforcement Learning-based Autonomous Traffic Engineering) that endows SDN-based architectures with self-adaptive, closed-loop decision-making capabilities to autonomously optimize traffic routing, load balancing, congestion control, and QoS enforcement. The proposed framework integrates Deep Q-Networks (DQN), Proximal Policy Optimization (PPO), and multi-agent RL (MARL) within a hierarchical SDN control architecture interfacing with real-time big data analytics pipelines built on Apache Kafka and Apache Spark Streaming. The system formulates TE as a Markov Decision Process (MDP), defining state spaces encompassing link utilization, end-to-end latency, packet loss rate, and queue depths, with reward functions engineered to balance throughput maximization, latency minimization, and fairness. Experimental evaluation on Mininet-based SDN emulation environments with synthetic and real-world Internet2 and G?ANT topology traffic traces demonstrates that RL-ATE achieves up to 38.7% improvement in network throughput, 42.3% reduction in end-to-end latency, and 31.5% improvement in link utilization efficiency compared to OSPF, ECMP, and heuristic SDN-TE baselines. The paper further discusses system scalability, convergence behavior, real-time applicability, and open challenges toward fully autonomous, intent-driven self-driving networks. These findings contribute substantively to the emerging research agenda at the intersection of deep reinforcement learning, big data analytics, SDN, and autonomous network management.

Links and sources

• PDF from original source

Comments