面向在线高流失率边缘计算的可靠性:一种无设备流水线方法

Nathan Vance, Md. Tahmid Rashid, D. Zhang, Dong Wang
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引用次数: 20

摘要

基于社会感知的边缘计算(SSEC)是一种新兴的应用范例,在传感应用中,人们在网络边缘协作进行数据采集和处理。虽然让人们保持在循环中可以带来巨大的好处,但引入的不可靠性和人员流失可能会对长时间运行的SSEC应用程序构成危险的稳定性威胁。在过去,这个问题是通过将这些责任转移到云或雾层中更可靠的服务器硬件来解决的,然而,这并没有充分利用边缘的能力。本文讨论了动态高流失率边缘系统的可靠边缘计算问题。我们开发了一种基于无设备管道的方法(DPA)来建立工作流,其中分析管道的各个阶段在边缘设备上完成,任何离开系统的设备都可以在不丢失数据的情况下进行替换。我们在一个执行对象检测应用程序的真实边缘计算系统上评估了我们的系统的性能,并证明它可以在吞吐量和错误恢复方面提供比传统计算卸载方案显著的性能提升。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Towards Reliability in Online High-Churn Edge Computing: A Deviceless Pipelining Approach
Social sensing based Edge Computing (SSEC) is an emerging application paradigm for rich context awareness in sensing applications in which people collaborate in both data acquisition and processing at the edge of the network. While keeping people in the loop can be an immense benefit, the unreliability and churn introduced can pose a dangerous stability threat for long-running SSEC applications. In the past this problem was addressed by offloading these responsibilities to more reliable server hardware in the cloud or a fog layer, however, this does not take full advantage of the power at the edge. In this paper we address the issue of reliable edge computing on dynamic high-churn edge systems. We develop a deviceless pipeline based approach (DPA) to establish workflows in which stages of the analysis pipeline are completed on edge devices, and any devices that leave the system can be replaced without data loss. We evaluate the performance of our system on a real-world edge computing system performing an object detection application and demonstrate that it can provide significant performance gains over traditional computation offloading schemes in terms of throughput and error recovery.
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