With an increase in the number of devices involved in the Industrial Internet, effectively combining the characteristics of industrial scenarios with an edge computing methodology for computation-intensive applications poses a critical challenge. This paper proposes an integrated architecture that allows industrial devices to offload tasks to cloud or edge servers. An offloading problem is also formulated into an energy-cost (EC) minimization problem while satisfying the deadline constraint. To solve the optimization problem, two types of offloading algorithms, namely ASO and Pro-ITGO, are proposed based on the integrated architecture. The ASO algorithm is a lightweight linear programming algorithm that includes subdeadline allocation, topology sorting, and task offloading sub-algorithms. The Pro-ITGO algorithm is a group intelligence heuristic algorithm that is derived from the original ITGO algorithm adapting the offloading scenarios of the Industrial Internet. Experimental results demonstrate that compared with state-of-the-art heuristic algorithms, the proposed algorithms can effectively reduce the energy consumption of industrial devices and cloud computing costs.

随着工业互联网中涉及的设备数量的增加，将工业场景的特征与用于计算密集型应用的边缘计算方法有效地结合起来构成了严峻的挑战。本文提出了一种集成架构，该架构允许工业设备将任务卸载到云或边缘服务器。在满足截止期限约束的同时，将卸载问题也化为能源成本（EC）最小化问题。为了解决优化问题，基于集成架构，提出了两种卸载算法，即ASO和Pro-ITGO。ASO算法是一种轻量级的线性规划算法，其中包括子截止期限分配，拓扑排序和任务卸载子算法。Pro-ITGO算法是一种组智能启发式算法，它是从原始ITGO算法衍生而来的，适用于工业Internet的卸载情况。实验结果表明，与最新的启发式算法相比，该算法可以有效降低工业设备的能耗和云计算成本。