Edge computing is a fast-growing computing paradigm where data is processed at the local site where it is generated, close to the end-devices. This can benefit a set of disruptive applications like autonomous driving, augmented reality, and collaborative machine learning, which produce incredible amounts of data that need to be shared, processed and stored at the edge to meet low latency requirements. However, edge storage poses new challenges due to the scarcity and heterogeneity of edge infrastructures and the diversity of edge applications. In particular, edge applications may impose conflicting constraints and optimizations that are hard to be reconciled on the limited, hard-to-scale edge resources. In this vision paper we argue that a new middleware for constrained edge resources is needed, providing a unified storage service for diverse edge applications. We identify programmability as a critical feature that should be leveraged to optimize the resource sharing while delivering the specialization needed for edge applications. Following this line, we make a case for eBPF and present the design for Griffin - a flexible, lightweight programmable edge storage middleware powered by eBPF.
翻译:边缘计算是一种快速增长的计算模式,在生成数据的当地地点处理数据,接近终端设备。这有利于一系列破坏性应用,如自主驱动、强化现实和协作机器学习,产生出惊人数量的数据,需要共享、处理和储存在边缘,以满足低潜伏要求。然而,边缘储存由于边缘基础设施稀缺和差异以及边缘应用的多样性而带来了新的挑战。特别是,边缘应用可能会造成相互冲突的制约和优化,这些限制和优化难以在有限的硬尺寸边缘资源上加以调和。在本愿景文件中,我们提出需要一个新的受限边缘资源中继软件,为各种边缘应用程序提供统一的存储服务。我们确定,在完成边缘应用所需的专门技术的同时,应利用编程能力来优化资源共享。沿着这条线,我们为eBPF, 提出一个有关eBPF的论证,并介绍Grifin的设计――一种由eBPF驱动的灵活、轻度程序可编程边缘存储中继器。