A growing number of critical workflow applications leverage a streamlined edge-hub-cloud architecture, which diverges from the conventional edge computing paradigm. An edge device, in collaboration with a hub device and a cloud server, often suffices for their reliable and efficient execution. However, task allocation in this streamlined architecture is challenging due to device limitations and diverse operating conditions. Given the inherent criticality of such workflow applications, where reliability and latency are vital yet conflicting objectives, an exact task allocation approach is typically required to ensure optimal solutions. As no existing method holistically addresses these issues, we propose an exact multi-objective task allocation framework to jointly optimize the overall reliability and latency of a workflow application in the specific edge-hub-cloud architecture. We present a comprehensive binary integer linear programming formulation that considers the relative importance of each objective. It incorporates time redundancy techniques, while accounting for crucial constraints often overlooked in related studies. We evaluate our approach using a relevant real-world workflow application, as well as synthetic workflows varying in structure, size, and criticality. In the real-world application, our method achieved average improvements of 84.19% in reliability and 49.81% in latency over baseline strategies, across relevant objective trade-offs. Overall, the experimental results demonstrate the effectiveness and scalability of our approach across diverse workflow applications for the considered system architecture, highlighting its practicality with runtimes averaging between 0.03 and 50.94 seconds across all examined workflows.

翻译：越来越多的关键工作流应用采用简化的边缘-枢纽-云架构，该架构与传统边缘计算范式有所不同。边缘设备与枢纽设备及云服务器协同工作，通常足以实现其可靠高效执行。然而，由于设备限制和多样化的运行条件，在这种简化架构中进行任务分配具有挑战性。鉴于此类工作流应用固有的关键性，其中可靠性和延迟是至关重要却又相互冲突的目标，通常需要采用精确的任务分配方法以确保获得最优解。由于现有方法未能整体性解决这些问题，我们提出了一种精确的多目标任务分配框架，以在特定的边缘-枢纽-云架构中联合优化工作流应用的整体可靠性和延迟。我们提出了一个全面的二进制整数线性规划模型，该模型考虑了各目标的相对重要性。它结合了时间冗余技术，同时纳入了相关研究中常被忽略的关键约束。我们使用相关的真实世界工作流应用以及结构、规模和关键性各异的合成工作流来评估我们的方法。在真实世界应用中，我们的方法在相关目标权衡下，相较于基线策略，在可靠性方面平均提升了84.19%，在延迟方面平均提升了49.81%。总体而言，实验结果证明了我们的方法在所考虑的系统架构下，对于多样化工作流应用的有效性和可扩展性，其运行时间在所有测试工作流中平均介于0.03至50.94秒之间，凸显了其实用性。