In this article, we propose a novel framework of mobile edge computing (MEC)-based hierarchical machine learning (ML) tasks distribution for the Industrial Internet of Things. It is assumed that a batch of ML tasks, such as anomaly detection, need to be executed timely in an MEC setting, where the devices have limited computing capability while the MEC server (MES) has rich computing resources. Thus, a small ML model for the device and a deep ML model for the MES are pretrained offline using historical data, and then they are deployed accordingly. However, offloading tasks to the MES introduces communications delay. Thus, each device must decide the portion of the tasks to offload to minimize the processing delay. Since the delay and the error of data processing are incurred by communications and ML computing, a joint optimization problem is formulated to minimize the total delay subject to the ML model complexity and inference error rate, data quality, computing capability at the device and MES, and communications bandwidth. A closed-form solution is derived analytically and an optimal offloading strategy selection algorithm is proposed. Insights are provided to understand the tradeoff between communications and ML computing in offloading decisions, and the effects of key parameters in the proposed algorithm are investigated. The numerical results demonstrate the effectiveness of the proposed algorithm.

中文翻译：

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IIoT 基于移动边缘计算的分层机器学习任务分配


在本文中，我们提出了一种用于工业物联网的基于移动边缘计算（MEC）的分层机器学习（ML）任务分配的新颖框架。假设需要在 MEC 设置中及时执行一批 ML 任务，例如异常检测，其中设备的计算能力有限，而 MEC 服务器（MES）具有丰富的计算资源。因此，设备的小型 ML 模型和 MES 的深度 ML 模型使用历史数据进行离线预训练，然后进行相应的部署。然而，将任务卸载到 MES 会导致通信延迟。因此，每个设备必须决定要卸载的任务部分，以最大限度地减少处理延迟。由于数据处理的延迟和错误是由通信和 ML 计算引起的，因此制定了联合优化问题，以最小化受 ML 模型复杂性和推理错误率、数据质量、设备和 MES 计算能力影响的总延迟，和通信带宽。解析得出封闭式解，并提出最优卸载策略选择算法。提供了见解以了解卸载决策中通信和机器学习计算之间的权衡，并研究了所提出算法中关键参数的影响。数值结果证明了该算法的有效性。