Task offloading for directed acyclic graph applications based on edge computing in Industrial Internet

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. (c) 2020 Elsevier Inc. All rights reserved.