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

EAGER：探索在整个计算连续体的约束下管理不确定性的智能服务：使用 SAGE 平台的案例研究

• 批准号: 2238064
• 负责人: Manish Parashar
• 金额: $ 30万
• 依托单位: University of Utah
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2022
• 资助国家: 美国
• 起止时间: 2022-10-01 至 2024-09-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2238064&HistoricalAwards=false
• 关键词: EAGER; Exploring; intelligent; services; managing

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.

先进的网络基础设施试图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程序（例如野火检测或个人安全），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）与异构动态计算环境中的可行条件以及数据可用性的不确定性相匹配是具有挑战性的。识别事件和加速响应关键取决于在不确定的操作条件下选择资源和配置服务的能力。该项目开发了在运行时对不可预见的事件做出反应的软件抽象，以及边缘和云之间的资源和计算路径的适应。这项研究由 SAGE 网络基础设施的独特功能驱动，作为案例研究，探索全国范围内网络边缘数据驱动的反应行为。该项目提出智能服务，以推动大型网络基础设施紧急情况下的计算优化。 该项目侧重于围绕三个相互关联的任务的资源管理和编程支持：（1）开发用于捕获边缘资源动态的模型； (2) 研究反应式分析的软件抽象； (3) 解决成本/效益权衡问题，以推动应用程序和资源在约束条件下的自主重新配置。其成果旨在直接扩展 SAGE 平台并提供用于创建紧急分析的工件，这将使 SAGE 用户和科学界受益。该奖项反映了 NSF 的法定使命，并通过使用基金会的智力优势和更广泛的评估进行评估，被认为值得支持。影响审查标准。