Increasing rate of progress in hardware and artificial intelligence (AI) solutions is enabling a range of software systems to be deployed closer to their users, increasing application of edge software system paradigms. Edge systems support scenarios in which computation is placed closer to where data is generated and needed, and provide benefits such as reduced latency, bandwidth optimization, and higher resiliency and availability. Users who operate in highly-uncertain and resource-constrained environments, such as first responders, law enforcement, and soldiers, can greatly benefit from edge systems to support timelier decision making. Unfortunately, understanding how different architecture approaches for edge systems impact priority quality concerns is largely neglected by industry and research, yet crucial for national and local safety, optimal resource utilization, and timely decision making. Much of industry is focused on the hardware and networking aspects of edge systems, with very little attention to the software that enables edge capabilities. This paper presents our work to fill this gap, defining a reference architecture for edge systems in highly-uncertain environments, and showing examples of how it has been implemented in practice.

翻译：硬件与人工智能（AI）解决方案的快速发展使得各类软件系统能够更贴近用户部署，从而推动了边缘软件系统范式的广泛应用。边缘系统支持将计算部署在数据生成与所需位置附近的场景，并提供降低延迟、优化带宽、提升弹性与可用性等优势。在高度不确定且资源受限环境中工作的用户（如急救人员、执法人员和士兵）可通过边缘系统显著获益，以支持更及时的决策制定。然而，当前工业界与学术界普遍忽视了对不同边缘系统架构方法如何影响关键质量属性的研究，而这对国家与地方安全、资源优化利用及及时决策至关重要。当前产业焦点多集中于边缘系统的硬件与网络层面，对实现边缘能力的软件关注严重不足。本文旨在填补这一空白，提出了一种面向高度不确定环境的边缘系统参考架构，并通过实例展示了其在实践中的具体实现。