Graph Neural Network based Service Function Chaining for Automatic Network Control

TL;DR

This paper introduces a graph neural network-based model for service function chaining in SDN and NFV environments, effectively utilizing network topology information and adapting to dynamic network changes.

Contribution

The paper proposes a novel GNN architecture for SFC that leverages topology information and can adapt to changing network topologies without retraining.

Findings

Outperformed previous DNN-based models in experiments.

Can be applied to new network topologies without re-designing or re-training.

Effectively captures network topology for improved SFC performance.

Abstract

Software-defined networking (SDN) and the network function virtualization (NFV) led to great developments in software based control technology by decreasing expenditures. Service function chaining (SFC) is an important technology to find efficient paths in network servers to process all of the requested virtualized network functions (VNF). However, SFC is challenging since it has to maintain high Quality of Service (QoS) even for complicated situations. Although some works have been conducted for such tasks with high-level intelligent models like deep neural networks (DNNs), those approaches are not efficient in utilizing the topology information of networks and cannot be applied to networks with dynamically changing topology since their models assume that the topology is fixed. In this paper, we propose a new neural network architecture for SFC, which is based on graph neural network (GNN) considering the graph-structured properties of network topology. The proposed SFC model consists of an encoder and a decoder, where the encoder finds the representation of the network topology, and then the decoder estimates probabilities of neighborhood nodes and their probabilities to process a VNF. In the experiments, our proposed architecture outperformed previous performances of DNN based baseline model. Moreover, the GNN based model can be applied to a new network topology without re-designing and re-training.