针对医疗物联网任务调度，提出分层强化学习框架以降低延迟。
Cloud-Fog-Edge Collaborative Computing for Sequential MIoT Workflow: A Two-Tier DDPG-Based Scheduling Framework
- 分两层决策：全局选层级，局部定节点，提升调度效率。
- 复杂度越高，相比基线性能越优，最大降低32%完成时间。
- 适合大规模医疗物联网系统，尤其长流程任务调度场景。
医疗物联网（MIoT）要求在异构云-雾-边架构上部署的连续医疗工作流具备严格的端到端延迟保障。将此类连续工作流调度以最小化完工时间（makespan）为目标是典型的NP难问题。为此，本文提出一种两级深度确定性策略梯度（DDPG）调度框架，将调度决策分解为分层过程：全局控制器负责选择计算层级（边缘、雾层或云端），而各层级的专用局部控制器则完成具体节点分配。核心优化目标是最小化工作流的完工时间。实验结果验证了该方法的有效性，在工作流复杂度上升时，相较基线方法表现出越来越显著的性能优势，凸显其学习长期有效策略的能力，对复杂、大规模的MIoT调度场景具有重要意义。
原文摘要 · Abstract (English)
The Medical Internet of Things (MIoT) demands stringent end-to-end latency guarantees for sequential healthcare workflows deployed over heterogeneous cloud-fog-edge infrastructures. Scheduling these sequential workflows to minimize makespan is an NP-hard problem. To tackle this challenge, we propose a Two-tier DDPG-based scheduling framework that decomposes the scheduling decision into a hierarchical process: a global controller performs layer selection (edge, fog, or cloud), while specialized local controllers handle node assignment within the chosen layer. The primary optimization objective is the minimization of the workflow makespan. Experiments results validate our approach, demonstrating increasingly superior performance over baselines as workflow complexity rises. This trend highlights the frameworks ability to learn effective long-term strategies, which is critical for complex, large-scale MIoT scheduling scenarios.
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