Edge Intelligence in Softwarized 6G: Deep Learning-enabled Network Traffic Predictions

The 6G vision is envisaged to enable agile network expansion and rapid deployment of new on-demand microservices (e.g., visibility services for data traffic management, mobile edge computing services) closer to the network’s edge IoT devices. However, providing one of the critical features of network visibility services, i.e., data flow prediction in the network, is challenging at the edge devices within a dynamic cloud-native environment as the traffic flow characteristics are random and sporadic. To provide the AI-native services for the 6G vision, we propose a novel edge-native framework to provide an intelligent prognosis technique for data traffic management in this paper. The prognosis model uses long short-term memory (LSTM)-based encoder-decoder deep learning, which we train on real time-series multivariate data records collected from the edge µ-boxes of a selected testbed network. Our result accurately predicts the statistical characteristics of data traffic and verifies the trained model against the ground truth observations. Moreover, we validate our novel framework with two performance metrics for each feature of the multivariate data.

I. Introduction

The successful commercialization of 5G networks paves the way for discussion on the next evolution towards 6G networks and defines it’s vision and requirements [1]. While 5G architecture is built on service-based architecture (SBA) [2], the vision of 6G hyper-flexible architecture revolves around the state-of-the-art artificial intelligence (AI)-native design that brings the intelligent decision-making abilities in futuristic applications of digital society, such as digital twin-enabled self-evolving innovative industries [3], [4]. The Third Generation Partnership Project (3GPP) is reportedly shifting towards new AI-inspired models to monitor and enhance the SBA performance [5].