5G 边缘和云端复杂 O-RAN 应用的分析与调度
Profiling and Scheduling Complex O-RAN Applications Across the 5G Edge and Cloud
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中文总结 AI 辅助
研究 5G 边缘和云端复杂 O-RAN 应用的分析与调度问题，提出 O-DAG 端到端框架，含 DagProfiler 工具等四个阶段，评估五种调度算法，HEFT 完工时间最低，SAGA 仿真差距可作状态诊断，所有工件已发布用于复现。
中文摘要 AI 辅助
O-RAN 范式将智能 RAN 控制分解为相互依赖的 AI/ML 功能管道，如流量预测、信号质量估计和切片调度，需在异构延迟和带宽约束下跨远边缘、近边缘和云资源的分散连续体执行。目前尚无综合方法来分析其执行成本、通过调度启发式将其映射到分散基础设施并在 5G 蜂窝条件下验证结果布局。本文提出 O-DAG 端到端框架，通过四个紧密耦合阶段弥补这一差距：DagProfiler 工具、参数化三层网络拓扑、SAGA 调度框架扩展及自定义 DAG 仿真模块。评估了五种调度算法在不同配置下的表现，HEFT 在所有配置中实现了最低完工时间，但调度器排名取决于工作负载。SAGA 仿真差距可作为一种状态诊断：负差距表明 HEFT 保守高估，正差距则暴露带宽竞争超出调度模型假设的通信受限状态。所有工件均已发布以实现可重复性。
英文摘要
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 far-edge, 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 per-task 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.
