Fog Computing for Deep Learning with Pipelines
Fog Computing for Deep Learning with Pipelines
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DOI：
10.1109/icfec57925.2023.00017
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发表时间：
2023-05
期刊：
影响因子：
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通讯作者：
Antero Vainio;Akrit Mudvari;Diego Kiedanski;Sasu Tarkoma;L. Tassiulas
中科院分区：
文献类型：
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作者：
Antero Vainio;Akrit Mudvari;Diego Kiedanski;Sasu Tarkoma;L. Tassiulas
在本文中，我们介绍了一种雾系统设计，用于处理从边缘设备（如移动、传感器和扩展（混合、增强、虚拟）现实设备）收集的数据。我们的系统使网络能够提供硬件加速处理器，用于对从远程位置收集的数据进行资源密集型计算，例如5G和超越移动网络。通过将繁重的计算划分为管道，并将其分配给边缘、雾和云中的处理器，我们的设计受益于云的处理能力，同时利用具有较低网络延迟的雾设备。我们实现了我们的设计，并将其用于计算机视觉的工业级深度学习模型的分布式训练和推理。我们将架构部署在基础设施中，包括支持gpu加速计算的云和边缘服务器。我们在各种部署设置中对管道进行基准测试，以研究它们带来的开销。我们的贡献是一个广域数据处理的新设计，一个实现该设计的框架，并提供开发在基础设施和硬件方面进行优化的应用程序的方法。这些贡献与我们的基准测试结果相辅相成，这些结果揭示了处理开销的潜在原因。
In this article, we introduce a fog system design for processing data collected from edge devices, such as mobile, sensor, and extended (mixed, augmented, virtual) reality equipment. Our system enables the network to provide hardware-accelerated processors for resource-intensive computations on data gathered from remote locations, such as 5G and beyond mobile networks. By splitting heavy computations into pipelines, and distributing them among processors in the edge, fog and the cloud, our design benefits from the processing power of the cloud, while utilizing fog devices with a lower network latency. We implement our design, and use it for distributed training and inference with industry-grade deep learning models for computer vision. We deploy our architecture in infrastructure including cloud and edge servers supporting GPU-accelerated computations. We benchmark pipelines in various deployment settings to study the overhead that they introduce. Our contributions are a new design for wide-area data processing, a framework that realizes this design and provides means of developing applications that are optimized in terms of infrastructure and hardware. These contributions are complemented with our benchmark results, which reveal the potential causes of processing overhead.