Associative Task Computation Offloading for Industrial Internet of Things

In this paper, we propose a computational offloading strategy tailored for multiple industrial equipment (IE) within an Industrial Internet of Things (IIoT) framework. Our primary focus is on addressing the computational offloading problem, specifically by modeling the associative relationship between tasks as a Directed Acyclic Graph (DAG). Initially, we transform the DAG task offloading problem into a Markov decision process (MDP). Subsequently, to derive the optimal offloading decision, we propose a novel approach termed DAG Task offloading with Message Passing Neural Network (MPNN) and Double Deep Q-Network (DAGTO-MAD). The proposed algorithm aims to min-imize the completion time of the task set. Moreover, we enhance the performance of the MPNN by adapting it to extract structural information from the DAG more efficiently. Simulation results demonstrate that our proposed algorithm achieves a shorter completion time compared to existing offloading algorithms across various environments. Additionally, our algorithm exhibits robust convergence and versatility, highlighting its effectiveness in real-application scenarios.