EAGER: Exploring intelligent services for managing uncertainty under constraints across the Computing Continuum: A case study using the SAGE platform

先进的网络基础设施寻求通过在边缘到云计算连续体中提供丰富的传感功能，使流数据成为科学界的一种模式。对于基于多个地理分布式数据源和复杂数据分析的社会应用，例如野火探测或个人安全，这些平台可以带来难以置信的变革。然而，将用户约束（响应时间、质量、能量）与异构和动态计算环境中的可行性相匹配是具有挑战性的，并且数据可用性存在不确定性。识别事件和加速响应在很大程度上取决于在不确定操作条件下选择资源和配置服务的能力。该项目开发的软件抽象可以在运行时对不可预见的事件做出反应，并适应边缘和云之间的资源和计算路径。这项研究是由SAGE网络基础设施的独特功能驱动的，作为一个案例研究，探索全国范围内网络边缘的数据驱动反应行为。该项目提出了智能服务，以推动大型网络基础设施紧急场景的计算优化。该项目侧重于围绕三个相互关联的任务进行资源管理和编程支持：(1)开发捕获边缘资源动态的模型；(2)研究响应式分析的软件抽象；(3)解决成本/收益权衡，以驱动约束下应用程序和资源的自主重新配置。结果旨在直接扩展SAGE平台，并为创建紧急分析提供工件，这将使SAGE用户和科学界受益。该奖项反映了美国国家科学基金会的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。

Advanced cyberinfrastructures seek to make streaming data a modality for the scientific community by offering rich sensing capabilities across the edge-to-Cloud Computing Continuum. For societal applications based on multiple geo-distributed data sources and sophisticated data analytics, such as wildfire detection or personal safety, these platforms can be incredibly transformative. However, it is challenging to match user constraints (response time, quality, energy) with what is feasible in a heterogeneous and dynamic computation environment coupled with uncertainty in the availability of data. Identifying events and accelerating responses critically depends on the capacity to select resources and configure services under uncertain operating conditions. This project develops software abstractions that react at runtime to unforeseen events, and adaptation of the resources and computing paths between the edge and the cloud. This research is driven by the unique capabilities of the SAGE cyberinfrastructure as a case study to explore data-driven reactive behaviors at the network's edge on a national scale. The project proposes intelligent services to drive computing optimization from urgent scenarios on large cyberinfrastructures. The project focuses on resource management and programming support around three interrelated tasks: (1) developing models for capturing the dynamics of edge resources; (2) researching software abstractions for reactive analytics; and (3) addressing cost/benefit tradeoffs to drive the autonomous reconfiguration of applications and resources under constraints. The outcomes are intended to directly extend the SAGE platform and provide artifacts for creating urgent analytics, which will benefit SAGE users and the scientific community.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.