Proactive AI-and-RAN Workload Orchestration in O-RAN Architectures for 6G Networks

TL;DR

This paper introduces a novel AI-and-RAN workload orchestration framework for 6G networks that dynamically manages resources to support both real-time RAN and AI workloads, improving efficiency and service quality.

Contribution

It proposes the CAORA framework based on O-RAN specs, integrating workload forecasting, anomaly detection, and reinforcement learning for proactive resource management.

Findings

Achieves near 99% fulfillment of RAN demands in simulations.

Supports dynamic AI workloads with high resource utilization.

Enhances system adaptability and service continuity.

Abstract

The vision of AI-RAN convergence, as advocated by the AI-RAN Alliance, aims to unlock a unified 6G platform capable of seamlessly supporting AI and RAN workloads over shared infrastructure. However, the architectural framework and intelligent resource orchestration strategies necessary to realize this vision remain largely unexplored. In this paper, we propose a Converged AI-and-ORAN Architectural (CAORA) framework based on O-RAN specifications, enabling the dynamic coexistence of real-time RAN and computationally intensive AI workloads. We design custom xApps within the Near-Real-Time RAN Intelligent Controller (NRT-RIC) to monitor RAN KPIs and expose radio analytics to an End-to-End (E2E) orchestrator via the recently introduced Y1 interface. The orchestrator incorporates workload forecasting and anomaly detection modules, augmenting a Soft Actor-Critic (SAC) reinforcement learning agent that proactively manages resource allocation, including Multi-Instance GPU (MIG) partitioning. Using real-world 5G traffic traces from Barcelona, our trace-driven simulations demonstrate that CAORA achieves near 99\% fulfillment of RAN demands, supports dynamic AI workloads, and maximizes infrastructure utilization even under highly dynamic conditions. Our results reveal that predictive orchestration significantly improves system adaptability, resource efficiency, and service continuity, offering a viable blueprint for future AI-and-RAN converged 6G systems.