Filtered by: Scheduling × Clear all

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

Yoonjae Hwang, Bhaskar Krishnamachari 2026-07-16
Scheduling × AI Communication Network Simulation Workload

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.

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HybridQC: Hardware-Grounded Simulation of Tightly Integrated Hybrid Quantum-Classical Systems

Panayiotis Christou, Shuwen Kan, Ying Mao 2026-07-16
Quant Ph Hardware Simulation Communication Scheduling × Runtime Workload

HybridQC addresses the problem that hybrid quantum-classical system performance is increasingly limited by classical control and communication, not quantum execution, and that existing tools fail to capture system-topology issues like controller bottlenecks and resource contention. The method introduces a topology-aware discrete-event simulator that models hybrid compute units as configurable graphs of classical and quantum devices, decomposes jobs into typed directed acyclic graphs, and supports interchangeable scheduling policies. Calibrated against live D-Wave and IBM processors, HybridQC achieves mean absolute percentage errors of 3.92%-8.04% for D-Wave QPU access time and 5.26%-19.01% for IBM quantum-seconds, and workload experiments show that balanced 10x HCU scaling improves makespan by only 2.19x-3.42x while scheduling policy changes shift makespan by up to 1.80x. This matters because HybridQC provides a systematic framework to evaluate topology, scheduling, and scaling limits of hybrid architectures before physical deployment, enabling researchers to identify bottlenecks and optimize resource allocation.

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