针对工业物联网中生成任务的实时性与能耗问题，提出智能调度算法提升边缘计算效率。
A Model Aware AIGC Task Offloading Algorithm in IIoT Edge Computing
- 基于多智能体强化学习，根据模型切换开销动态分配任务。
- 实验显示平均延迟降6.98%，能耗降7.12%，任务完成率升3.72%。
- 适合高负载、任务动态变化的智能制造边缘场景使用。
工业互联网（IIoT）与人工智能生成内容（AIGC）的融合为智能制造带来新机遇，但同时也面临计算密集型任务与低时延需求的挑战。传统基于云的生成模型难以满足IIoT环境中的实时性要求，而边缘计算通过任务卸载可有效降低延迟。然而，AIGC任务的动态性、模型切换延迟及资源限制对边缘环境提出更高要求。为此，本文提出一种面向IIoT边缘计算环境的AIGC任务卸载框架，首次考虑了模型切换带来的时延与能耗。在该框架下，多个智能体协作将动态的AIGC任务卸载至部署不同生成模型的最优边缘服务器。设计了一种基于多智能体深度确定性策略梯度（MADDPG-MATO）的模型感知卸载算法，以最小化延迟和能耗。实验结果表明，该算法在四组实验中（模型数量从3到6），平均延迟降低6.98%，能耗下降7.12%，任务完成率提升3.72%，证明其在动态、高负载的IIoT环境中具备鲁棒性与高效性。
原文摘要 · Abstract (English)
The integration of the Industrial Internet of Things (IIoT) with Artificial Intelligence-Generated Content (AIGC) offers new opportunities for smart manufacturing, but it also introduces challenges related to computation-intensive tasks and low-latency demands. Traditional generative models based on cloud computing are difficult to meet the real-time requirements of AIGC tasks in IIoT environments, and edge computing can effectively reduce latency through task offloading. However, the dynamic nature of AIGC tasks, model switching delays, and resource constraints impose higher demands on edge computing environments. To address these challenges, this paper proposes an AIGC task offloading framework tailored for IIoT edge computing environments, considering the latency and energy consumption caused by AIGC model switching for the first time. IIoT devices acted as multi-agent collaboratively offload their dynamic AIGC tasks to the most appropriate edge servers deployed with different generative models. A model aware AIGC task offloading algorithm based on Multi-Agent Deep Deterministic Policy Gradient (MADDPG-MATO) is devised to minimize the latency and energy. Experimental results show that MADDPG-MATO outperforms baseline algorithms, achieving an average reduction of 6.98% in latency, 7.12% in energy consumption, and a 3.72% increase in task completion rate across four sets of experiments with model numbers ranging from 3 to 6, it is demonstrated that the proposed algorithm is robust and efficient in dynamic, high-load IIoT environments.
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