GNNetSlice: A GNN-based performance model to support network slicing in B5G networks

Abstract

Network slicing is gaining traction in Fifth Generation (5G) deployments and Beyond 5G (B5G) designs. In a nutshell, network slicing virtualizes a single physical network into multiple virtual networks or slices, so that each slice provides a desired network performance to the set of traffic flows (source-destination pairs) mapped to it. The network performance, defined by specific Quality of Service (QoS) parameters (latency, jitter and losses), is tailored to different use cases, such as manufacturing, automotive or smart cities. A network controller determines whether a new slice request can be safely granted without degrading the performance of existing slices, and therefore fast and accurate models are needed to efficiently allocate network resources to slices. Although there is a large body of work of network slicing modeling and resource allocation in the Radio Access Network (RAN), there are few works that deal with the implementation and modeling of network slicing in the core and transport network. In this paper, we present GNNetSlice, a model that predicts the performance of a given configuration of network slices and traffic requirements in the core and transport network. The model is built leveraging Graph Neural Networks (GNNs), a kind of Neural Network specifically designed to deal with data structured as graphs. We have chosen a data-driven approach instead of classical modeling techniques, such as Queuing Theory or packet-level simulations due to their balance between prediction speed and accuracy. We detail the structure of GNNetSlice, the dataset used for training, and show how our model can accurately predict the delay, jitter and losses of a wide range of scenarios, achieving a Symmetric Mean Average Percentage Error (SMAPE) of 5.22%, 1.95% and 2.04%, respectively