The emerging edge-hub-cloud paradigm has enabled the development of innovative latency-critical cyber-physical applications in the edge-cloud continuum. However, this paradigm poses multiple challenges due to the heterogeneity of the devices at the edge of the network, their limited computational, communication, and energy capacities, as well as their different sensing and actuating capabilities. To address these issues, we propose an optimal scheduling approach to minimize the overall latency of a workflow application in an edge-hub-cloud cyber-physical system. We consider multiple edge devices cooperating with a hub device and a cloud server. All devices feature heterogeneous multicore processors and various sensing, actuating, or other specialized capabilities. We present a comprehensive formulation based on continuous-time mixed integer linear programming, encapsulating multiple constraints often overlooked by existing approaches. We conduct a comparative experimental evaluation between our method and a well-established and effective scheduling heuristic, which we enhanced to consider the constraints of the specific problem. The results reveal that our technique outperforms the heuristic, achieving an average latency improvement of 13.54% in a relevant real-world use case, under varied system configurations. In addition, the results demonstrate the scalability of our method under synthetic workflows of varying sizes, attaining a 33.03% average latency decrease compared to the heuristic.

翻译：新兴的边缘-枢纽-云范式促进了边缘-云连续体中创新型延迟关键型信息物理应用的发展。然而，由于网络边缘设备的异构性、其有限的计算、通信和能源容量，以及不同的传感与执行能力，该范式带来了多重挑战。为解决这些问题，我们提出了一种最优调度方法，以最小化边缘-枢纽-云信息物理系统中工作流应用的整体延迟。我们考虑了多个边缘设备与一个枢纽设备及云服务器的协作。所有设备均配备异构多核处理器，并具有多种传感、执行或其他专用能力。我们提出了一个基于连续时间混合整数线性规划的全面建模框架，封装了现有方法常忽略的多个约束条件。我们进行了对比实验评估，将我们的方法与一种经过验证且有效的调度启发式算法进行比较，该启发式算法已针对特定问题的约束进行了增强。结果显示，我们的技术优于启发式方法，在相关实际用例中，在不同系统配置下实现了平均延迟降低13.54%。此外，结果证明了我们的方法在不同规模合成工作流下的可扩展性，与启发式方法相比，平均延迟降低了33.03%。