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.
翻译:在当今由IoT和其他装置产生大量数据的物联网时代,边缘计算已成为低纬度数据处理的突出范例,但应用可能具有不同的潜伏要求:某些对潜伏敏感的处理操作可能需要在边缘进行,同时可以在云层上进行延缓操作,而不必占用潜在的有限边际计算资源。为了实现这一点,我们设想了一个将计算资源分布在边缘和云中的环境。在本文中,我们介绍了CLEDGE(CLOUD + EDGE)的设计,这是一个以信息为中心的混合云端框架,目的是最大限度地在时间上完成由不同悬浮要求的应用所卸载的计算任务。CLEDGE的设计是受混合现实研究人员所面临的联网挑战驱动的。我们的评估表明,CLEGE可以实时完成超过90%的卸载任务,同时完成少量的间接费用。