CC* Integration-Small: Network-Aware Edge Computing for Real-time Wildfire Detection

CC* Integration-Small：用于实时野火检测的网络感知边缘计算

• 批准号: 2346755
• 负责人: Batyr Charyyev
• 金额: $ 50万
• 依托单位: Board of Regents, NSHE, obo University of Nevada, Reno
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2024
• 资助国家: 美国
• 起止时间: 2024-05-01 至 2026-04-30
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2346755&HistoricalAwards=false
• 关键词: CC; Integration; Small; Network; Aware

The proliferation of Internet of Things (IoT) devices has facilitated the development of various scientific applications, from smart city initiatives to environmental hazard monitoring systems. In most of these applications, swift processing of data captured by IoT sensors is crucial to prevent natural disasters in a timely manner. While conventional cloud-based data processing pipelines offer cost-effective and performance-efficient solutions, it is often challenging to meet stringent performance requirements of hazard monitoring systems such as low latency and high bandwidth. Edge computing emerges as a compelling solution to bridge this gap by bringing computational processing closer to the data source. This project develops an edge computing framework tailored to address the computational requirements of time-sensitive distributed scientific applications such as wildfire monitoring. The edge computing framework has the potential to benefit other domains beyond wildfire monitoring, such as autonomous vehicles and emergency response systems.The project develops an edge computing framework that optimizes the task scheduling problem by combining high precision system monitoring with comprehensive application profiling. It develops a scalable resource monitoring system to monitor the status of compute resources (e.g., edge servers and cloud instances) and network resources using lightweight monitoring agents and P4 programmable network devices. Additionally, the project conducts application profiling to extract essential metrics regarding resource utilization and execution time of tasks across various edge server and cloud instance configurations. The scheduling is formulated as a multi-objective optimization problem, and various optimization methods such as mixed-integer linear programming, genetic algorithms, and heuristic methods are explored. Finally, the team targets a wildfire detection project (AlertWildfire) as a use case to demonstrate the effectiveness of the proposed framework. This project is jointly funded by Office of Advanced Cyberinfrastructure, the Established Program to Stimulate Competitive Research (EPSCoR), and the Division of Computer and Network Systems.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.

物联网（IoT）设备的扩散促进了各种科学应用的开发，从智能城市计划到环境危害监测系统。在大多数这些应用中，IoT传感器捕获的数据迅速处理对于及时防止自然灾害至关重要。尽管常规的基于云的数据处理管道提供了具有成本效益和性能高效的解决方案，但满足危险监测系统（例如低潜伏期和高带宽）的正式性能要求通常具有挑战性。边缘计算是一种令人信服的解决方案，可以通过使计算处理更接近数据源来弥合这一差距。 该项目开发了一个量身定制的边缘计算框架，以解决时间敏感的分布式科学应用（例如野火监测）的计算要求。边缘计算框架有可能使其他领域超越野火监控，例如自动驾驶汽车和应急响应系统。该项目开发了一个边缘计算框架，通过将高精度系统监控与全面的应用程序配置文件相结合，从而优化任务调度问题。它开发了一个可扩展的资源监视系统，以使用轻型监视代理和P4可编程网络设备来监视计算资源（例如边缘服务器和云实例）和网络资源的状态。此外，该项目还进行了申请分析，以提取有关资源利用和执行时间的基本指标，以及各种边缘服务器和云实例配置的任务时间。调度被称为多目标优化问题，并探索了各种优化方法，例如混合构成线性编程，遗传算法和启发式方法。最后，该团队以野火检测项目（AlertWildFire）为目标，以证明拟议框架的有效性。该项目由高级网络基础设施办公室，启发竞争性研究的既定计划（EPSCOR）共同资助，并分配了计算机和网络系统的部门。这项奖项反映了NSF的法定任务，并被认为是值得通过基金会的知识分子优点和更广泛的审查标准通过评估来通过评估来支持的。