Profiling and Scheduling Complex O-RAN Applications Across the 5G Edge and Cloud

O-DAG addresses the lack of an integrated methodology for profiling and scheduling O-RAN AI/ML pipelines as DAGs across far-edge, near-edge, and cloud resources. The framework combines DagProfiler for task profiling, a three-tier network topology, an extension of the SAGA scheduler, and a MintEDGE-based simulation module. Experimental evaluation of five scheduling algorithms for slice scheduling across 5K–50K UEs shows HEFT achieves the lowest makespan in all configurations, but scheduler rankings are workload-dependent. This matters because O-DAG enables validated, reproducible placement of complex O-RAN applications under realistic 5G constraints, revealing regime-dependent scheduling behavior and model limitations.