Aligning Routing With Service Intent in Logical Networks: A QoS-Driven Graph Attention Reinforcement Learning Framework

Abstract

Network virtualization enables the creation of multiple logical networks on shared physical infrastructure, each supporting homogeneous traffic with a dedicated Quality of Service (QoS) objective. This shifts the routing problem from arbitrating among heterogeneous flows to holistically orchestrating traffic toward a single service intent. However, existing routing schemes, including those based on Deep Reinforcement Learning (DRL), lack mechanisms to align forwarding decisions with these high-level intents, leading to a performance gap. To bridge it, we propose QGARL, a QoS-driven Graph Attention Reinforcement Learning framework. Its core is an intent-conditioned attention mechanism that dynamically guides a DRL agent’s perception of the network graph based on the service’s QoS intent, enabling the learning of intent-aware routing policies without per-service algorithm redesign. Extensive experiments demonstrate that QGARL consistently outperforms state-of-the-art baselines in intent-weighted QoS utility across diverse services and topologies. This work establishes intent alignment as a guiding principle for routing in logical networks and provides a practical, learning-based framework to achieve it.