Service Function Chaining and Traffic Steering in SDN using Graph Neural Network

In the network softwarization, Network Function Virtualisation (NFV) has shifted the standard of network services deployment and management for telecommunication. However, the allocation of physical resources to the Virtualised Network Functions (VNFs) dynamically, efficiently, and autonomously is one of the key tasks to achieve this objective. Network designing is a serious factor for the deployment of VNFs to create a Service Function Chaining (SFC) with efficient resource utilization and optimal path in the edge computing environment. The development of a self-driven Software-Defined Network (SDN) also requires an intelligent network design, especially to find optimum routing patterns for traffic steering that meet the goals set by administrators. But unfortunately, the current network designing methods do not fulfill the desired requirement for precise estimations of related performance metrics. Recently, the application of Artificial Intelligence (AI) is considered by the research community to operate and control the network. The Graph Neural Network (GNN) is adopted as an AI solution in the field of the network because GNN can understand the complex relationship between network traffic features, routing, and topology to produce an accurate estimation of relevant performance metrics. In this paper, we propose the implementation of the Knowledge-Defined Networking (KDN) system based on Graph Neural Network (GNN) to predict the optimal path for SFC deployment and traffic steering. The proposed system is evaluated using the complete virtualized environment which is composed of ONOS, OpenStack, and open-source MANO (OSM).