Abstract
In recent years, cloud computing and fog computing have appeared one after the other, as promising technologies for augmenting the computing capability of devices locally. By offloading computational tasks to fog servers or cloud servers, the time for task processing decreases greatly. Thus, to guarantee the Quality of Service (QoS) of smart manufacturing systems, fog servers are deployed at network edge to provide fog computing services. In this paper, we study the following problems in a mixed computing system: (1) which computing mode should be chosen for a task in local computing, fog computing or cloud computing? (2) In the fog computing mode, what is the execution sequence for the tasks cached in a task queue? Thus, to solve the problems above, we design a Software-Defined Network (SDN) framework in a smart factory based on an Industrial Internet of Things (IIoT) system. A method based on Computing Mode Selection (CMS) and execution sequences based on the task priority (ASTP) is proposed in this paper. First, a CMS module is designed in the SDN controller and then, after operating the CMS algorithm, each task obtains an optimal computing mode. Second, the task priorities can be calculated according to their real-time performance and calculated amount. According to the task priority, the SDN controller sends a flow table to the SDN switch to complete the task transmission. In other words, the higher the task priority is, the earlier the fog computing service is obtained. Finally, a series of experiments and simulations are performed to evaluate the performance of the proposed method. The results show that our method can achieve real-time performance and high reliability in IIoT.
Highlights
In the context of Industry 4.0, Internet of Things (IoT), cloud computing, Big Data and other advanced technology provides technical support for the development of intelligent manufacturing [1,2,3,4]
ASTP makes a novel execution sequence for the tasks according to the task priority, while ASCM still adopts the conventional execution sequence
We study the adaptive computing optimization problem in an Industrial IoT (IIoT) enable fog computing system for various tasks processing
Summary
In the context of Industry 4.0, Internet of Things (IoT), cloud computing, Big Data and other advanced technology provides technical support for the development of intelligent manufacturing [1,2,3,4]. Humans, machines and things are connected by Industrial IoT (IIoT) [5,6,7,8]. The cloud provides a service platform for data processing and data analysis [9]. Cloud computing provides a solid foundation for the realization of intelligent manufacturing. Massive data transmission will cause network congestion and network bandwidth bottlenecks have become an obstacle that baffles the development of cloud computing; even worse, network delay reduce the Quality of Service (QoS) for cloud computing [10,11,12]
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