Distributed Diffusion Policy for Cooperative Resource Orchestration in IIoT Edge Networks

With the rapid proliferation of Industrial Internet of Things (IIoT) devices, massive Delay Sensitive and Computation Intensive (DSCI) tasks are generated. Traditional Mobile Edge Computing (MEC) systems face limitations like inter-cell interference at cell edges, degrading Quality of Service (QoS). To address this, Cooperative Access Edge Networks (CAEN) enable dynamic Access Point (AP) clusters for enhanced transmission reliability in IIoT. However, challenges arise from multi-user interference, bandwidth contention, dynamic environments, and heterogeneous resources, complicating joint resource orchestration. This paper proposes EdgeDiffuse, a diffusion-enhanced distributed resource orchestration algorithm, which optimizes task offloading selection, transmission power control, and computational resource allocation to minimize long term task completion time while promoting system load balancing. EdgeDiffuse enables adaptive and hierarchical coordination between user agents and edge servers. It integrates diffusion models under a Multi-Agent Deep Reinforcement Learning (MADRL) framework for improved policy exploration in high dimensional offloading decision spaces, and further uses convex optimization for server side resource allocation. Experimental results demonstrate that EdgeDiffuse achieves 28.17% reduction in task completion time, 7.40% improvement in task transmission rates, and 15.71% enhancement in load balancing compared to advanced baselines, showcasing superior performance in multi-user and resource constrained scenarios.