School of Mechanical and Automotive Engineering, South China University of Technology, Guangzhou 510641, China.
Sensors (Basel). 2019 Feb 28;19(5):1023. doi: 10.3390/s19051023.
Fog computing provides computation, storage and network services for smart manufacturing. However, in a smart factory, the task requests, terminal devices and fog nodes have very strong heterogeneity, such as the different task characteristics of terminal equipment: fault detection tasks have high real-time demands; production scheduling tasks require a large amount of calculation; inventory management tasks require a vast amount of storage space, and so on. In addition, the fog nodes have different processing abilities, such that strong fog nodes with considerable computing resources can help terminal equipment to complete the complex task processing, such as manufacturing inspection, fault detection, state analysis of devices, and so on. In this setting, a new problem has appeared, that is, determining how to perform task scheduling among the different fog nodes to minimize the delay and energy consumption as well as improve the smart manufacturing performance metrics, such as production efficiency, product quality and equipment utilization rate. Therefore, this paper studies the task scheduling strategy in the fog computing scenario. A task scheduling strategy based on a hybrid heuristic (HH) algorithm is proposed that mainly solves the problem of terminal devices with limited computing resources and high energy consumption and makes the scheme feasible for real-time and efficient processing tasks of terminal devices. Finally, the experimental results show that the proposed strategy achieves superior performance compared to other strategies.
雾计算为智能制造提供计算、存储和网络服务。然而，在智能工厂中，任务请求、终端设备和雾节点具有很强的异构性，例如终端设备的不同任务特征：故障检测任务具有很高的实时性需求；生产调度任务需要大量的计算；库存管理任务需要大量的存储空间等等。此外，雾节点具有不同的处理能力，具有相当计算资源的强雾节点可以帮助终端设备完成复杂的任务处理，例如制造检测、故障检测、设备状态分析等。在这种情况下，出现了一个新的问题，即如何在不同的雾节点之间执行任务调度，以最小化延迟和能耗，提高智能制造的性能指标，如生产效率、产品质量和设备利用率。因此，本文研究了雾计算场景中的任务调度策略。提出了一种基于混合启发式（HH）算法的任务调度策略，主要解决了计算资源有限和能耗高的终端设备的问题，并使该方案能够实时高效地处理终端设备的任务。最后，实验结果表明，所提出的策略与其他策略相比具有更好的性能。
