Emerging IoT-enabled cyber-physical applications demand low-latency, energy-efficient, and reliable execution across resource-constrained edge devices with heterogeneous multicore processors and diverse sensing and actuating capabilities, in collaboration with a hub device and a cloud server. These workflow-based applications comprise interdependent tasks that must be executed under stringent deadline, reliability, capability, memory, storage, and energy constraints. Given their critical nature, exact optimization is necessary to obtain optimal schedules that ensure dependable operation. Existing scheduling approaches, both exact and heuristic, fail to jointly address all these objectives and constraints. To this end, we propose an exact multi-objective and multi-constrained workflow scheduling approach for edge-hub-cloud cyber-physical systems, based on continuous-time mixed integer linear programming. The proposed formulation jointly optimizes latency, energy, and reliability, while holistically addressing timing and resource constraints. To enhance reliability while avoiding the overhead of unnecessary task replicas, it selectively employs task duplication. We evaluate our approach against a widely used heuristic, which we extend to ensure a fair and meaningful comparison, using a real-world IoT workflow and synthetic task graphs of varying sizes, across different system configurations and objective trade-offs. The proposed method consistently outperforms the heuristic, achieving up to 29.83%, 33.96%, and 28.49% average improvements in latency, energy, and reliability, respectively, while attaining practical runtimes. Overall, the experimental results demonstrate the effectiveness of our approach under various system configurations and objective trade-offs, and show its practical scalability to task graphs of sizes relevant to the targeted applications and system architecture.

翻译：新兴的物联网赋能信息物理应用要求在资源受限的边缘设备（配备异构多核处理器及多样化感知与执行能力）与枢纽设备和云服务器协同下，实现低延迟、高能效且可靠的执行。这些基于工作流的应用包含相互依赖的任务，必须在严格的截止时间、可靠性、能力、内存、存储和能源约束下执行。鉴于其关键性，需要精确优化以获得确保可靠运行的最优调度。现有调度方法（无论是精确算法还是启发式算法）均无法同时应对所有上述目标与约束。为此，我们提出一种基于连续时间混合整数线性规划的精确多目标多约束工作流调度方法，适用于边缘-枢纽-云信息物理系统。该公式化方法在整体处理时序与资源约束的同时，联合优化延迟、能量和可靠性。为避免不必要的任务副本开销并提升可靠性，它选择性地采用任务复制技术。我们使用实际物联网工作流和不同规模合成任务图，在不同系统配置与目标权衡下，将所提方法与广泛使用的启发式算法（我们对其进行了扩展以确保公平且有意义的比较）进行对比评估。所提方法始终优于启发式算法，在延迟、能量和可靠性方面分别获得高达29.83%、33.96%和28.49%的平均改进，同时实现了实际可行的运行时间。总体而言，实验结果证明了所提方法在不同系统配置与目标权衡下的有效性，并展示了其对目标应用及系统架构相关任务图规模的实际可扩展性。