AweRAN: Making a Case for Application-aware Radio Access Network Slicing

As communications service providers ponder ways to cater to the diverse traffic requirements of mobile applications that range from the classic telephony to modern augmented reality (AR)-related use cases, the traditional quality of service (QoS)-based radio resource management (RRM) techniques for RAN slicing that are agnostic to the intrinsic workings of applications can result in a poor quality of experience (QoE) for the end-user. We argue that in addition to QoS, RAN slicing strategies should also consider QoE for efficient resource utilization. However, without comprehensively understanding the interplay between QoS, QoE and how various RRM techniques can potentially influence them, it is impossible to incorporate QoE-driven feedback for resource allocation. Consequently, in this work, we conduct a first-of-its-kind in-depth experimental campaign on an O-RAN compliant 5G cellular testbed to  evaluate the performance of the QoE metrics of three varied applications---voice, cloud gaming, and AR---under various RAN slice configurations. We discuss the key findings of this elaborate study, and motivate the need for a QoE-aware RRM framework for RAN slicing.