Energy-Efficient VNF Deployment for Graph-Structured SFC Based on Graph Neural Network and Constrained Deep Reinforcement Learning
Siyu Qi, Shuopeng Li, Shaofu Lin, Mohand Yazid Saidi, Ken Chen
Abstract
Network Function Virtualization (NFV), which decouples network functions from hardware and transforms them into hardware-independent Virtual Network Functions (VNF), is a crucial technology for many emerging networking domains, such as 5G, edge computing and data-center network. Service Function Chaining (SFC) is the ordered set of VNFs. The VNF deployment problem is to find the optimal deployment strategy of VNFs in SFC while guaranteeing the Service-Level Agreements (SLAs). Existing VNF deployment researches mainly focus on sequences of VNFs without energy consideration. However, with the rapid development of application requirement, the SFCs evolve from sequence to dynamic graph and the service providers become more and more sensitive to the energy consumption in NFV. Therefore, in this paper, we identify the Energy-efficient Graph-structured SFC problem (EG-SFC) and formulate it as a Combinatorial Optimization Problem (COP). Benefiting from the recent advances in machine learning for COP, we propose an end-to-end Graph Neural Network (GNN) based on constrained Deep Reinforcement Learning (DRL) method to solve EG-SFC. Our method leverages the Graph Convolutional Network (GCN) to represent the Q-network of Double Deep Q-Network (DDQN) in DRL. The mask mechanism is proposed to deal with the resources constraints in COP. The experimental results show that the proposed method can deal with unseen SFC graphs and achieve better performances than greedy algorithm and traditional DDQN.