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Tasks Scheduling and Resource Allocation in Fog Computing Based on Containers for Smart Manufacturing

机译:基于容器的智能制造中雾计算中的任务调度与资源分配

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摘要

Fog computing has been proposed as an extension of cloud computing to provide computation, storage, and network services in network edge. For smart manufacturing, fog computing can provide a wealth of computational and storage services, such as fault detection and state analysis of devices in assembly lines, if the middle layer between the industrial cloud and the terminal device is considered. However, limited resources and low-delay services hinder the application of new virtualization technologies in the task scheduling and resource management of fog computing. Thus, we build a new task-scheduling model by considering the role of containers. Then, we construct a task-scheduling algorithm to ensure that the tasks are completed on time and the number of concurrent tasks for the fog node is optimized. Finally, we propose a reallocation mechanism to reduce task delays in accordance with the characteristics of the containers. The results showed that our proposed task-scheduling algorithm and reallocation scheme can effectively reduce task delays and improve the concurrency number of the tasks in fog nodes.
机译:提出雾计算作为云计算的扩展,以在网络边缘中提供计算,存储和网络服务。对于智能制造,如果考虑了工业云和终端设备之间的中间层,雾计算可以提供大量的计算和存储服务,例如装配线中设备的故障检测和状态分析。但是,有限的资源和低延迟的服务阻碍了新的虚拟化技术在雾计算的任务调度和资源管理中的应用。因此,我们通过考虑容器的作用来构建新的任务调度模型。然后,我们构造一个任务调度算法,以确保任务按时完成,并且优化了雾节点的并发任务数。最后,我们提出了一种重新分配机制,以根据容器的特性减少任务延迟。结果表明,本文提出的任务调度算法和重新分配方案可以有效地减少任务延迟,提高雾节点中任务的并发数量。

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