An Intelligent eUPF for Time-Sensitive Path Selection in B5G Edge Networks

TL;DR

This paper introduces an AI-driven eUPF design using Deep Q-Networks for real-time, reliable path selection in B5G networks, enhancing traffic management and reducing latency.

Contribution

It presents a novel DQN-based path selection method combined with passive delay measurement using eBPF, improving B5G network performance.

Findings

DQN agent outperforms random baseline in latency and stability

Passive delay measurement enables low-cost, accurate delay estimation

Reinforcement learning proves effective for network control in B5G

Abstract

In Beyond 5G (B5G) networks, intelligent, flexible traffic management is essential to meet the stringent speed and reliability requirements of new applications. This paper presents an improved User Plane Function (eUPF) design that uses a Deep Q-Network (DQN) agent for real-time path selection between Multi-access Edge Computing (MEC) and cloud endpoints. The path selection problem is formulated as a Partially Observable Markov Decision Process (POMDP). We propose a novel passive delay measurement method that uses eBPF programs to link TEID-based timestamps in GTP-U traffic, allowing for low-cost delay estimation without active testing. Experiments show that the DQN agent substantially outperforms a random baseline, with lower average latency, more stable rewards, and more reliable low-delay path choices. These results demonstrate the effectiveness of AI-driven control in B5G core networks and the promise of reinforcement learning for modern network management.