In today's era of Internet of Things (IoT), where massive amounts of data are produced by IoT and other devices, edge computing has emerged as a prominent paradigm for low-latency data processing. However, applications may have diverse latency requirements: certain latency-sensitive processing operations may need to be performed at the edge, while delay-tolerant operations can be performed on the cloud, without occupying the potentially limited edge computing resources. To achieve that, we envision an environment where computing resources are distributed across edge and cloud offerings. In this paper, we present the design of CLEDGE (CLoud + EDGE), an information-centric hybrid cloud-edge framework, aiming to maximize the on-time completion of computational tasks offloaded by applications with diverse latency requirements. The design of CLEDGE is motivated by the networking challenges that mixed reality researchers face. Our evaluation demonstrates that CLEDGE can complete on-time more than 90% of offloaded tasks with modest overheads.

中文翻译：

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CLEDGE：基于以信息为中心的网络的混合云边缘计算框架

在当今物联网 (IoT) 时代，物联网和其他设备会产生大量数据，边缘计算已成为低延迟数据处理的重要范例。然而，应用程序可能有不同的延迟要求：某些延迟敏感的处理操作可能需要在边缘执行，而延迟容忍操作可以在云上执行，而不占用可能有限的边缘计算资源。为了实现这一点，我们设想了一个计算资源分布在边缘和云产品中的环境。在本文中，我们介绍了 CLEDGE (CLoud + EDGE) 的设计，这是一种以信息为中心的混合云边缘框架，旨在最大限度地按时完成由具有不同延迟要求的应用程序卸载的计算任务。CLEDGE 的设计灵感来自混合现实研究人员面临的网络挑战。我们的评估表明，CLEDGE 可以以适度的开销按时完成 90% 以上的卸载任务。