ORAN-HAutoscaling: A Scalable and Efficient Resource Optimization Framework for Open Radio Access Networks with Performance Improvements

Open Radio Access Networks (ORANs) are transforming the traditional telecommunications landscape by offering more flexible, vendor-independent solutions. Unlike previous systems, which relied on rigid, vertical configurations, ORAN introduces network programmability that is AI-driven and horizontally scalable. This shift is facilitated by modern container orchestrators, such as Kubernetes and Red Hat OpenShift, which simplify the development and deployment of components such as gNB, CU/DU, and RAN Intelligent Controllers (RICs). While these advancements help reduce costs by enabling shared infrastructure, they also create new challenges in meeting ORAN’s stringent latency requirements, especially when managing large-scale xApp deployments. Near-RTRICs are responsible for controlling xApps that must adhere to tight latency constraints, often less than one second. Current orchestration methods fail to meet these demands, as they lack the required scalability and long latencies. Additionally, non-API-based E2AP (over SCTP) further complicates the scaling process. To address these challenges, we introduce ORAN-HAutoscaling, a framework designed to enable horizontal scaling through Kubernetes. This framework ensures that latency constraints are met while supporting large-scale xApp deployments with optimal resource utilization. ORAN-HAutoscaling dynamically allocates and distributes xApps into scalable pods, ensuring that central processing unit (CPU) utilization remains efficient and latency is minimized, thus improving overall performance.