Bui Khoi Anh, Yoo Myungsik
Department of Electronic Engineering, Soongsil University, Seoul 06978, Republic of Korea.
School of Electronic Engineering, Soongsil University, Seoul 06978, Republic of Korea.
Sensors (Basel). 2025 May 4;25(9):2904. doi: 10.3390/s25092904.
Designing an efficient task offloading system is essential in the Industrial Internet of Things (IIoT). Owing to the limited computational capability of IIoT devices, offloading tasks to edge servers enhances computational efficiency. When an edge server is overloaded, it may experience interruptions, preventing it from serving local devices. Existing studies mainly address interruptions by rerouting, rescheduling, or implementing reactive strategies to mitigate their impact. In this study, we introduce an interruption-aware proactive task offloading framework for IIoT. We develop a load-based interruption model in which the probability of server interruption is formulated as an exponential function of the total computational load, which provides a more realistic estimation of service availability. This framework employs Multi-Agent Advantage Actor-Critic (MAA2C)-a simple yet efficient approach that enables decentralized decision-making while handling large action spaces and maintaining coordination through the centralized critic to make adaptive offloading decisions, taking into account edge availability, resource limitations, device cooperation, and interruptions. Experimental results show that our approach effectively reduces the average total service delay by optimizing the tradeoff between system delay and availability in IIoT networks. Additionally, we investigate the impact of various system parameters on performance under an interruptible edge task offloading scenario, providing valuable insights into how these parameters influence the overall system behavior and efficiency.
设计一个高效的任务卸载系统在工业物联网（IIoT）中至关重要。由于IIoT设备的计算能力有限，将任务卸载到边缘服务器可提高计算效率。当边缘服务器过载时，可能会出现中断，从而使其无法为本地设备提供服务。现有研究主要通过重新路由、重新调度或实施反应式策略来解决中断问题，以减轻其影响。在本研究中，我们为IIoT引入了一种感知中断的主动任务卸载框架。我们开发了一个基于负载的中断模型，其中服务器中断的概率被公式化为总计算负载的指数函数，这为服务可用性提供了更现实的估计。该框架采用多智能体优势行动者-评论家（MAA2C）——一种简单而有效的方法，它能够进行分散决策，同时处理大型动作空间，并通过集中评论家保持协调，以做出自适应卸载决策，同时考虑边缘可用性、资源限制、设备协作和中断情况。实验结果表明，我们的方法通过优化IIoT网络中系统延迟和可用性之间的权衡，有效降低了平均总服务延迟。此外，我们研究了在可中断边缘任务卸载场景下各种系统参数对性能的影响，为这些参数如何影响整体系统行为和效率提供了有价值的见解。
