GCN-TD: A Learning-based Approach for Service Function Chain Deployment on the Fly

Abstract

Network function virtualization (NFV) has emerged as a promising paradigm for transforming network functions from dedicated hardware to software middleboxes, which can substantially improve service agility and reduce management cost. Benefiting from NFV, service function chains (SFCs) can be formulated through the orchestration of virtual network functions (VNFs). One of the most significant issues for infrastructure providers (InPs) is to determine how to deploy SFCs under the limited resources of underlying infrastructure in an online manner. In this paper, we propose a novel reinforcement learning-based approach named GCN-TD for online SFC deployment problem, aiming to maximize the long-term average revenue. GCN-TD combines the advantages of the graph convolutional network (GCN) which gives the comprehensive representations for network states and the temporal-difference (TD) learning which makes online deployment decisions for SFC requests. Experimental results demonstrate that GCN-TD outperforms other candidate algorithms in terms of the long-term average revenue and acceptance ratio.