CRII: CNS: Toward a Scalable Geo-distributed Data Analytics System

CRII：CNS：迈向可扩展的地理分布式数据分析系统

基本信息

批准号：
2153422
负责人：
Kwangsung Oh
金额：
$ 17.22万
依托单位：
University of Nebraska at Omaha
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：
2022-07-01 至 2024-06-30
项目状态：
已结题

来源：
https://www.nsf.gov/awardsearch/showAward?AWD_ID=2153422&HistoricalAwards=false
关键词：
CRII CNS Toward Scalable Geo

项目摘要

This award is funded in whole or in part under the American Rescue Plan Act of 2021 (Public Law 117-2).Many applications that automate people's daily activities rely on the cloud to serve users around the globe. For example, latency-sensitive services, such as Netflix, Uber, and Airbnb, utilize cloud resources in Amazon’s geo-distributed data centers instead of managing their own data centers. These cloud application providers must analyze highly diffuse, large-scale data, including users’ activities logs and system logs — i.e., geo-distributed analytics (GDA). GDA tasks affect user experiences, system health, and operating cost; they must be completed in a timely and cost-efficient manner. However, achieving this is challenging due to the complexities of heterogeneous cloud resources and unpredictable changes in the cloud environments and applications. This project aims to design and implement a novel GDA system that determines and manages the optimal cloud resource configurations, with consideration of the wide-area network (WAN) and diverse compute resources. In addition, the system will make task scheduling decisions to process GDA queries in a timely and cost-efficient manner considering available cloud resources, input data locations, application goals, and query execution dynamics. Thus, GDA applications will be able to achieve cost-performance goals with minimal effort, while addressing and overcoming the challenges above. The successful completion of this project will advance the understanding of (1) how GDA workloads can be accurately predicted using historical data, and (2) how diverse and heterogeneous cloud resources can be optimally allocated and efficiently utilized in GDA. A design and prototype implementation will be publicly available in the form of an open-source system. The outcomes of this research will allow researchers and software developers across several scientific, commercial, and social domains to easily run and evaluate diverse GDA algorithms. The research outcomes of this project will also be integrated into the university course curriculum for both undergraduate- and graduate-level students. This project will also open diverse opportunities that broaden participation and diversity in STEM education.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.

该奖项是根据2021年《美国救援计划法》（公共法第117-2）的全部或部分资助的。自动化人们的日常活动的许多申请依赖于云为全球用户提供服务。例如，Netflix，Uber和Airbnb等潜伏期敏感的服务在亚马逊的地理分布的数据中心中利用云资源，而不是管理自己的数据中心。这些云应用程序提供商必须分析高度分散的大规模数据，包括用户的活动日志和系统日志，即地理分配分析（GDA）。 GDA任务会影响用户体验，系统健康和运营成本；它们必须及时且具有成本效益的方式完成。但是，由于异质云资源的复杂性以及云环境和应用程序中无法预测的变化，实现这一目标是具有挑战性的。该项目旨在设计和实施一个新颖的GDA系统，该系统通过考虑广阔的区域网络（WAN）和潜水员计算资源来决定和管理最佳的云资源配置。此外，考虑可用的云资源，输入数据位置，应用程序目标和查询执行动力学，系统将制定任务计划决策以及时且具有成本效益的方式处理GDA查询。这就是GDA应用程序将能够以最小的努力来实现成本绩效的目标，同时解决并克服上述挑战。该项目的成功完成将提高（1）如何使用历史数据准确预测GDA工作负载，以及（2）在GDA中可以最佳地分配和有效地利用多样化和异质云资源。设计和原型实现将以开源系统的形式公开可用。这项研究的结果将使几个科学，商业和社会领域的研究人员和软件开发人员可以轻松运行和评估潜水员GDA算法。该项目的研究成果也将纳入本科和研究生级学生的大学课程课程中。该项目还将为扩大STEM教育的参与和多样性的潜力机会开放。该奖项反映了NSF的法定任务，并通过使用基金会的知识分子优点和更广泛的影响审查标准来评估，被认为是珍贵的支持。