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EdgeFlow: Open-Source Multi-layer Data Flow Processing in Edge Computing for 5G and Beyond

机译：EdgeFlow：用于5G及更高版本的边缘计算中的开源多层数据流处理

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摘要

Edge computing has evolved to be a promising avenue to enhance system computing capability by offloading processing tasks from the cloud to edge devices. In this article, we propose a multi-layer edge computing framework called EdgeFlow. In this framework, different nodes ranging from edge devices to cloud data centers are categorized into corresponding layers and cooperate for data processing. EdgeFlow can deal with the trade-off between the computing and communication capabilities so that the tasks can be assigned to each layer optimally. At the same time, resources are carefully allocated throughout the whole network to mitigate performance fluctuation. The proposed open-source data flow processing framework is implemented on a platform that can emulate various computing nodes in multiple layers and corresponding network connections. Evaluated on the face recognition scenario, EdgeFlow can significantly reduce task finish time and perform more tolerance to run-time variations, compared with pure cloud computing, pure edge computing, and Cloudlet. Potential applications of EdgeFlow, including network function virtualization, Internet of Things, and vehicular networks, are also discussed at the end of this article.
机译：通过将处理任务从云转移到边缘设备，边缘计算已成为增强系统计算能力的有前途的途径。在本文中，我们提出了一种称为EdgeFlow的多层边缘计算框架。在此框架中，从边缘设备到云数据中心的不同节点被分类到相应的层中，并协作进行数据处理。 EdgeFlow可以处理计算能力和通信能力之间的折衷，因此可以将任务最佳地分配给每一层。同时，在整个网络中仔细分配资源以减轻性能波动。所提出的开源数据流处理框架是在可以模拟多层和相应网络连接中的各种计算节点的平台上实现的。与纯云计算，纯边缘计算和Cloudlet相比，对人脸识别方案进行评估后，EdgeFlow可以显着减少任务完成时间，并对运行时变化表现出更大的容忍度。本文末尾还将讨论EdgeFlow的潜在应用，包括网络功能虚拟化，物联网和车辆网络。

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  • 来源
    《IEEE Network》 |2019年第2期|166-173|共8页
  • 作者

    Yao Chao; Wang Xiaoyang; Zheng Zijie; Sun Guangyu; Song Lingyang;

  • 作者单位

    Peking Univ, Sch Elect Engn & Comp Sci, Beijing, Peoples R China;

    Peking Univ, Beijing, Peoples R China;

    Peking Univ, Sch Elect Engn & Comp Sci, Beijing, Peoples R China;

    Peking Univ, CECA, Beijing, Peoples R China;

    Peking Univ, Sch Elect Engn & Comp Sci, Beijing, Peoples R China;

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