Proactive VNF Scaling and Placement in 5G O-RAN Using ML
Khalid Ali, Manar Jammal
Abstract
5G networks are expected to support various services and applications with more stringent latency, reliability, and bandwidth requirements than previous generations. Open Radio Access Networks (O-RAN) have been proposed to meet these requirements. The O-RAN Alliance assumes O-RAN components to be Virtualized Network Functions (VNFs). Furthermore, O-RAN allows employing Machine Learning (ML) solutions to tackle challenges in resource management. However, intelligently managing resources for O-RAN can be proved challenging. Network providers need to scale resources in response to incoming traffic dynamically. Elastically allocating resources provides higher flexibility, reduces OPerational EXpenditure (OPEX), and increases resource utilization. In this work, we propose and evaluate an elastic VNF orchestration framework for O-RAN. The proposed system consists of a traffic forecasting-based dynamic scaling scheme using ML and a Reinforcement Learning (RL) based VNF placement policy. The models are evaluated based on their predictive capabilities subject to all Service-Level Agreements.