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工业 4.0 中基于依赖任务计算的高效能量和完成时间卸载算法。

Efficient energy and completion time for dependent task computation offloading algorithm in industry 4.0.

机构信息

Electrical Engineering Dept., Port Said University, Port Said, Egypt.

出版信息

PLoS One. 2021 Jun 8;16(6):e0252756. doi: 10.1371/journal.pone.0252756. eCollection 2021.

Abstract

Rapid technological development has revolutionized the industrial sector. Internet of Things (IoT) started to appear in many fields, such as health care and smart cities. A few years later, IoT was supported by industry, leading to what is called Industry 4.0. In this paper, a cloud-assisted fog-networking architecture is implemented in an IoT environment with a three-layer network. An efficient energy and completion time for dependent task computation offloading (ET-DTCO) algorithm is proposed, and it considers two quality-of-service (QoS) parameters: efficient energy and completion time offloading for dependent tasks in Industry 4.0. The proposed solution employs the Firefly algorithm to optimize the process of the selection-offloading computing mode and determine the optimal solution for performing tasks locally or offloaded to a fog or cloud considering the task dependency. Moreover, the proposed algorithm is compared with existing techniques. Simulation results proved that the proposed ET-DTCO algorithm outperforms other offloading algorithms in minimizing energy consumption and completion time while enhancing the overall efficiency of the system.

摘要

快速的技术发展彻底改变了工业领域。物联网 (IoT) 开始出现在医疗保健和智慧城市等许多领域。几年后,物联网得到了业界的支持,从而催生了所谓的工业 4.0。本文在具有三层网络的物联网环境中实现了一种云辅助雾网络架构。提出了一种高效能量和完成时间相关任务计算卸载 (ET-DTCO) 算法,该算法考虑了两个服务质量 (QoS) 参数:工业 4.0 中相关任务的高效能量和完成时间卸载。所提出的解决方案采用萤火虫算法优化选择-卸载计算模式的过程,并确定在考虑任务相关性的情况下在本地执行任务或卸载到雾或云的最佳解决方案。此外,还将所提出的算法与现有技术进行了比较。仿真结果证明,所提出的 ET-DTCO 算法在最小化能耗和完成时间方面优于其他卸载算法,同时提高了系统的整体效率。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/cf4a/8186806/953f52706af7/pone.0252756.g001.jpg

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