Dependency‐Aware Computation Offloading and Resource Allocation in Industrial Edge Networks

Edge computing provides an effective solution for delay‐sensitive services in the industrial Internet of Things (IIoT). However, the dependencies between tasks in the IIoT present challenges for task offloading and resource allocation in edge networks. This paper addresses the problem of computation offloading for dependent tasks in IIoT environments. We propose a dependent task model and jointly optimize subtask offloading decisions, transmit power, and local computation frequency for each terminal device, considering inter‐regional mobile edge computing (MEC) collaboration. The objective is to minimize the weighted sum of task execution delay and energy consumption for terminal devices. Due to the combinatorial characteristic of offloading decisions among all tasks and their coupling with resource allocation, the proposed optimization problem is formulated as a mixed‐integer nonlinear programming problem. To solve this problem, we introduce a single‐offload candidate‐space reduction rule under fixed transmit power and local CPU frequency. Based on this rule, a low‐complexity Gibbs sampling algorithm is proposed to update the offloading decisions. Subsequently, for a given offloading decision, a deep deterministic policy gradient (DDPG)–based joint optimization algorithm is introduced to obtain the optimal transmit power and local CPU computation frequency. Simulation results indicate that the proposed algorithm achieves better performance than the considered benchmark schemes under the evaluated small‐ to moderate‐scale IIoT scenarios.