Fairness-Aware Deterministic Joint Offloading and Scheduling for Industrial Edge Computing

Industrial edge computing in time-sensitive, heterogeneous environments faces significant challenges in delivering deterministic, low-latency, and fair task offloading under mixed-criticality traffic. Existing solutions struggle with unpredictable delays, resource contention, and the joint optimization of offloading and scheduling. In this work, we propose a task queue mapping mechanism-based architecture that incorporates formal mixed-deterministic traffic modeling under time-sensitive networking, enabling unified computation and network resource management. The joint task offloading and traffic scheduling problem is rigorously formulated as a two-stage Markov decision process with embedded fairness constraints. To address the strong coupling and computational complexity, we develop a fairness-aware deterministic offloading and resource scheduling (FA-DORS) framework, a hierarchical multiagent deep deterministic policy gradient algorithm, enhanced by a contribution-based local reward mechanism to ensure adaptive resource allocation and fairness. Extensive experimental results demonstrate that in the proposed architecture, FA-DORS achieves lower task latency, higher completion rates, improved resource utilization, and enhanced transmission determinism while ensuring fairness in multitask offloading compared to baseline methods. In particular, FA-DORS achieves an 8.89%–39.15% reduction in average end-to-end delay and up to 39.73% reduction in average jitter under high load, compared with the baseline method.