A Service Function Chain Traffic Steering Path Algorithm Based on Graph Convolutional Network and Deep Q-Network

With the rise of applications such as the Internet of Things (IoT) and Virtual Reality (VR), there is an increasing demand for stringent service latency and quality of service requirements, which has led to a shift in network service deployment from cloud to edge, giving rise to Mobile Edge Computing (MEC) architectures.In MEC environments, network infrastructure is distributed near users, allowing access to local networks in real time.However, dynamically orchestrating Service Function Chains (SFCs) presents a significant challenge, especially in resource-constrained settings where maximizing SFC deployments while maintaining low latency is essential for service providers' revenue optimization.To address this challenge, this paper proposes an intelligent SFC orchestration strategy, termed GCN-DQN, which combines Graph Convolutional Networks (GCNs) and Deep Q-Networks (DQNs).The GCN-DQN framework is designed to optimize the request acceptance rate while ensuring compliance with stringent low-latency requirements.To achieve this, the GCN-DQN strategy is designed to perceive network structure, resource availability, and SFCspecific information, enabling optimized decision-making for SFC traffic steering paths.Performance evaluations demonstrate that the proposed algorithm outperforms existing methods in SFC request acceptance rates under various network loads.