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

Abstract

The O-RAN paradigm decomposes intelligent RAN control into pipelines of interdependent AI/ML functions, including traffic prediction, signal quality estimation, and slice scheduling, that must execute across a dispersed continuum of faredge, near-edge, and cloud resources under heterogeneous latency and bandwidth constraints. Despite the natural expression of these pipelines as Directed Acyclic Graphs (DAGs), no integrated methodology exists to profile their execution costs, map them onto dispersed infrastructure via scheduling heuristics, and validate the resulting placement under 5G cellular conditions. We present O-DAG, an end-to-end framework that closes this gap through four tightly coupled stages: (1) DagProfiler, a new open-source tool that instruments O-RAN Slice Scheduler and extracts pertask instruction counts and per-edge communication volumes; (2) a parameterized three-tier network topology encoding far-edge (DU, RIC), near-edge (edge), and cloud nodes with realistic link bandwidths; (3) an extension of the SAGA scheduling framework and (4) a custom DAG simulation module built on the MintEDGE simulator. We evaluate five scheduling algorithms (HEFT, MCT, MinMin, MaxMin, Duplex) for a slice scheduling application across various configurations spanning 5K–50K UEs, 2–20 cells, and 2–10 network slices. HEFT achieves the lowest makespan in all configurations, but scheduler rankings are workload-dependent. The SAGA–simulation gap serves as a regime diagnostic: negative gaps (up to −1.72%) identify compute-dominated configurations where HEFT overestimates conservatively, while a positive gap (+0.64%) at low slice counts exposes a communication-bound regime where bandwidth contention exceeds the scheduling model’s assumptions. All artifacts are released for reproducibility.