PSASlicing: Perpetual SLA-Aware Reinforcement Learning for O-RAN Slice Management

Network slicing has been widely recognized as one of the flagship use cases for Open Radio Access Network (O-RAN), enabling the provisioning of isolated network services over a shared physical infrastructure. Each slice is characterized by a set of distinct service level agreements (SLAs) tailored to meet the needs of various industries and applications. At the same time, industry-critical applications often require strict adherence to the SLA even in the worst-case scenarios. However, existing network slicing strategies merely incorporate SLA violations as penalties within the reward function, thus failing to consistently ensure perpetual SLA compliance. To address these challenges, this paper introduces PSASlicing, an intelligent resource allocation system designed for RAN slice management across the access network. More specifically, PSASlicing introduces a new reinforcement learning algorithm for maximizing resource utilization while perpetually guaranteeing the diverse SLA requirements across slices. Furthermore, PSASlicing also incorporates a trace-driven network emulator that effectively replicates the dynamic behavior of cellular networks by integrating a transition model with real-world data from an over-the-air 5G Standalone testbed. A comprehensive experimental evaluation showcases that PSASlicing achieves an average resource savings of approximately 24.0% when compared to the state-of-the-art, while guaranteeing no SLA violations.