A Two‐Timescale O‐RAN‐Native Framework for Priority‐Aware Resource Orchestration and Task Offloading in Edge Computing Networks

ABSTRACT Edge computing networks must support heterogeneous services with sharply different latency, reliability, and bandwidth requirements under time‐varying traffic, limited radio capacity, and distributed compute resources. These challenges become more critical when orchestration decisions must be coordinated across multiple edge regions with strict response‐time constraints. This paper proposes a two‐timescale O‐RAN‐native framework for priority‐aware resource orchestration and task offloading in edge computing networks. The framework follows the O‐RAN control hierarchy by assigning long‐timescale workload analysis, policy adaptation, and service‐aware resource planning to the Non‐RT RIC, whereas a Near‐RT RIC xApp performs fast runtime decisions for task offloading, bandwidth assignment, and compute allocation according to instantaneous network conditions. To support cooperative control among distributed edge nodes, the proposed design combines graph‐based internode context modeling with a priority‐aware decision mechanism that captures queue state, service urgency, link quality, and available processing resources. This architecture enables rapid local adaptation without losing global policy consistency. Simulation results under dynamic multiservice workloads show that the proposed framework reduces service delay, improves completion reliability for urgent traffic, and increases overall resource efficiency compared with representative baseline methods.