AI-driven Service-aware Real-time Slicing for beyond 5G Networks

Wide network softwarization is creating fertile ground for the application of novel concepts in the management of the deployed network functions. This allows a drastic shift for the applications hosted on top of the network, as instead of configuring their behaviour to match the network status (network-aware applications) the technology can shift to a self-organizing network that adapts to the hosted applications (service-aware network). In this work, we design, develop and evaluate such a service-aware approach for the telecommunications network. By employing Machine Learning, we are able to classify and predict traffic exchanges made over the network, and appropriately dynamically allocate the slices in the network in real-time. We use as a reference platform the OpenAirInterface framework, and the FlexRAN controller for programming the slice decisions at the RAN level, and evaluate our scheme under real-world settings in a testbed environment.