Proximal Policy Optimization for Latency-Aware Service Function Chain Placement in Edge-Enabled Networks

Abstract

Modern networked systems are progressively adopting network virtualization and Service Function Chaining (SFC) to deliver scalable, flexible and low-latency services. A central challenge in this context is the Service Function Chain Placement Problem (SFC-PP), which involves efficiently placing Virtualized Network Functions (VNFs) under stringent resource constraints and Quality of Service (QoS) requirements in dynamic network environments. To address this, we propose an enhanced Proximal Policy Optimization (PPO) framework for resource-aware and latency-sensitive SFC placement in edge-enabled networks. Our approach incorporates network performance metrics into the PPO learning pipeline through delay-aware reward design, resource-aware state representation and feasibility-aware action masking, thereby improving policy stability and infrastructure utilization. Extensive evaluations on multiple topologies including RNET, NSFNET, GBN and GEANT2 demonstrate that the proposed PPO significantly outperforms the Random Selection (RS), Q-Learning (QL) and Deep Q-Network (DQN) baselines, achieving up to 31% higher service acceptance, with reduced service latency and improved resource efficiency. A healthcare-oriented case study demonstrates the applicability of the proposed framework in mission-critical and latency-sensitive service environments. These results highlight PPO as a robust and scalable solution for intelligent service orchestration in dynamic network environments.