CRII: CNS: With Uncertainty Comes Opportunity: Providing Best-Effort Edge Services with Uncertain and Limited Resources

CRII：CNS：不确定性带来机遇：利用不确定且有限的资源提供尽力而为的边缘服务

• 批准号: 2104337
• 负责人: Zheng Song
• 金额: $ 17.44万
• 依托单位: Regents of the University of Michigan - Dearborn
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2021
• 资助国家: 美国
• 起止时间: 2021-09-01 至 2024-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2104337&HistoricalAwards=false
• 关键词: CRII; CNS; Uncertainty; Comes; Opportunity

The goal of this project is to create a runtime system for providing best-effort edge services. Different from clouds with manageable and redundant resources, many edge environments are opportunistic and feature uncertain and limited resources. As a result, edge services cannot guarantee Quality of Service (QoS) for their clients, which is a critical obstacle of creating current and emerging edge computing applications. Existing solutions fall short, as they optimize the overall QoS by allocating bottleneck resources competed for by services. In contrast, this project is based on the insight that the uncertainty of edge environments also brings opportunity so the equivalent functions that do not consume the same resources can satisfy the same service request. Hence, this project will explore how to systematically improve QoS by dynamically orchestrating equivalent functions that consume underutilized resources.The technologies developed during the course of this project will accelerate large-scale applications of edge computing in emerging domains like autonomous vehicles, smart homes, and Industry 4.0. A particular objective is to make it possible to use any kinds of hardware devices in edge computing environments. The software infrastructure developed for this project will be used for instructional purposes to create new and enrich existing courses on distributed systems and edge computing. Executed in the Detroit Metropolitan area, this project provides an excellent opportunity for outreach to local diverse communities, thus accelerating end-user adoption of edge computing technologies and attracting underrepresented groups into computing study and research.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.

该项目的目标是创建一个运行时系统，以提供尽力而为的边缘服务。与具有可管理和冗余资源的云不同，许多边缘环境是机会主义的，具有不确定和有限的资源。因此，边缘服务无法保证其客户端的服务质量（QoS），这是创建当前和新兴边缘计算应用的关键障碍。现有的解决方案不足，因为它们通过分配服务竞争的瓶颈资源来优化整体QoS。相比之下，这个项目是基于这样一种认识，即边缘环境的不确定性也带来了机会，因此不消耗相同资源的等效功能可以满足相同的服务请求。因此，该项目将探索如何通过动态编排消耗未充分利用资源的等效功能来系统地提高QoS，在该项目过程中开发的技术将加速边缘计算在自动驾驶汽车，智能家居和工业4.0等新兴领域的大规模应用。一个特定的目标是使在边缘计算环境中使用任何类型的硬件设备成为可能。为该项目开发的软件基础设施将用于教学目的，以创建新的和丰富现有的分布式系统和边缘计算课程。该项目在底特律大都会区执行，为与当地不同社区的联系提供了一个绝佳的机会，从而加速了终端用户对边缘计算技术的采用，并吸引了代表性不足的群体参与计算学习和研究。该奖项反映了NSF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。

项目成果

OS 3 : The Art and the Practice of Searching for Open-Source Serverless Functions

OS 3：搜索开源无服务器功能的艺术与实践

• DOI: 10.1109/percomworkshops56833.2023.10150370
• 发表时间: 2023
• 期刊: 2023 IEEE International Conference on Pervasive Computing and Communications Workshops and other Affiliated Events (PerCom Workshops
• 作者: Bhatnagar, Sarvesh;Li, Zhengquan;Song, Zheng;Tilevich, Eli
• 通讯作者: Tilevich, Eli

Quality of Information Matters: Recommending Web Services for Performance and Utility

• DOI: 10.1109/cloudcom55334.2022.00016
• 发表时间: 2022-12
• 期刊: 2022 IEEE International Conference on Cloud Computing Technology and Science (CloudCom)
• 作者: Zheng Song;Owen Rowader;Zheng R. Li;Maryam Tello;E. Tilevich

Applying Project-based Learning to Improve Computer Networks Courses: An Experience Report

应用基于项目的学习来改进计算机网络课程：经验报告

• DOI: 10.1109/educon52537.2022.9766750
• 发表时间: 2022
• 期刊: EDUCON
• 作者: Song, Zheng;Shah, Nidhi;Guo, Jinhua;Zhu, Qiang
• 通讯作者: Zhu, Qiang