定义高度不确定环境中边缘系统的参考架构
Defining a Reference Architecture for Edge Systems in Highly-Uncertain Environments
AI总结：
针对高度不确定、资源受限环境下边缘软件架构研究不足的问题，本文提出一种参考架构并给出实践实现示例。
摘要：
硬件和人工智能（AI）解决方案日益加快的发展速度，正使一系列软件系统能够部署在更靠近用户的位置，从而增加了边缘软件系统范式的应用。边缘系统支持将计算放置在更靠近数据产生和被需求之处的场景，并提供降低延迟、优化带宽以及更高弹性和可用性等优势。在高度不确定且资源受限环境中行动的用户，例如急救人员、执法人员和士兵，可以极大受益于边缘系统，以支持更及时的决策。遗憾的是，理解边缘系统的不同架构方法如何影响优先质量关注点，在很大程度上被工业界和研究界忽视，而这对于国家和地方安全、最优资源利用以及及时决策至关重要。工业界大多关注边缘系统的硬件和网络方面，很少关注实现边缘能力的软件。本文介绍了我们为填补这一空白所做的工作，定义了一种面向高度不确定环境中边缘系统的参考架构，并展示了其在实践中如何实现的示例。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
