Collaborative Research: Elements: FaaSr: Enabling Cloud-native Event-driven Function-as-a-Service Computing Workflows in R

协作研究:要素:FaaSr:在 R 中启用云原生事件驱动的函数即服务计算工作流程

基本信息

  • 批准号:
    2311123
  • 负责人:
  • 金额:
    $ 50万
  • 依托单位:
  • 依托单位国家:
    美国
  • 项目类别:
    Standard Grant
  • 财政年份:
    2023
  • 资助国家:
    美国
  • 起止时间:
    2023-09-15 至 2026-08-31
  • 项目状态:
    未结题

项目摘要

This project develops FaaSr, a new software that will facilitate the programming and deployment of scientific computing applications written in the R language in Function-as-a-Service (FaaS) cloud computing infrastructures. The FaaS model of cloud computing supports dynamic, on-demand execution of computing functions in servers that are automatically provisioned and managed, in a way that is both cost-effective and scalable: users do not need to manage cloud servers (including on-demand scaling) nor pay for idle time of unutilized servers. The FaaS model thus has much potential for reducing the complexity and cost of performing scientific computing in cloud infrastructures. To date, however, FaaS platforms have been primarily designed to support Web-based applications, resulting in a major gap between existing FaaS platforms and the scientific community. This gap is particularly evident in the environmental sciences, where R is the focal programming language. This is because: 1) there is no native support for the R language in FaaS platforms, and 2) each FaaS platform has a unique interface to deploy and manage workflows consisting of multiple functions, thereby creating barriers for users to develop and deploy applications on one or more FaaS platforms. This project bridges this gap by developing open-source software to accelerate the adoption of event-driven FaaS workflows for scientific applications. The FaaSr software will be distributed as an easy-to-install R package and will provide simple interfaces to programmers, while supporting multiple open-source and commercial cloud computing infrastructures. The software will support a wide range of scientific computing applications, in particular those that require dynamic event-driven processing (such as forecasting and continuous data quality) in environmental science subfields (including ecology and biodiversity). Ultimately, the project aims to develop scalable, generalizable, and robust workflows that will advance the capacity, practice, and training opportunities for ecological forecasting, an active area of scientific research poised to significantly increase predictive capacity for effective environmental decision-making and management. The FaaSr software developed in this project will greatly expand the adoption of FaaS cloud computing infrastructure. Currently, there are significant challenges to be overcome before scientific applications written in the R language can fully realize the potential of FaaS platforms, because R is not supported natively, and because different platforms have different, incompatible programming interfaces. Furthermore, scientific applications require workflows consisting of multiple functions that are executed dynamically and communicate by exchanging data as files in cloud storage. Different FaaS platforms have different programming interfaces to accomplish these capabilities, leading to increased complexity for developers and users. This collaborative, interdisciplinary project overcomes these challenges by integrating expertise in distributed systems, ecology, and forecasting together to design and implement software that: is driven by scientific computing use cases; creates easy-to-use interfaces; and builds on state-of-the-art distributed computing techniques and frameworks. Specifically, the FaaSr software will make multiple novel technical contributions, including: 1) it will allow end users to program a workflow at a high abstraction level and with the R language; 2) it will include a unified, easy-to-use interface for handling event invocation and argument parsing that hides the complexity of programming for multiple FaaS interfaces from developers, while supporting multiple FaaS frameworks, including GitHub Actions, OpenWhisk, IBM Cloud Functions, and Amazon Web Services Lambda; 3) it will include an easy-to-use interface for handling cloud data storage and access that hides low-level details (e.g., access endpoints and credentials) using de-facto standard interfaces and file formats; and 4) it will implement a unified approach to compose directed acyclic graph workflows that can be automatically mapped to programming interfaces supported by different FaaS platforms. Experiences with the design, implementation, and deployment of FaaSr will contribute new techniques and technologies in distributed/cloud computing, with lakes and reservoirs studied as part of this project providing a realistic testbed for assessing performance, extensibility, and availability of the software. Furthermore, the team will build on and expand its existing program for cross-disciplinary research exchanges of undergraduate and graduate students that provide novel training at the intersection of computer science, freshwater science, and ecosystem modeling.This award by the NSF Office of Advanced Cyberinfrastructure is jointly supported by the NSF Directorate for Biological Sciences.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.
该项目开发了 FaaSr,这是一款新软件,将促进在功能即服务 (FaaS) 云计算基础设施中用 R 语言编写的科学计算应用程序的编程和部署。云计算的FaaS模型支持在自动配置和管理的服务器中动态、按需执行计算功能,以一种既经济高效又可扩展的方式:用户不需要管理云服务器(包括按需扩展)也不为未使用的服务器的空闲时间付费。因此,FaaS 模型在降低云基础设施中执行科学计算的复杂性和成本方面具有巨大潜力。然而迄今为止,FaaS 平台主要设计用于支持基于 Web 的应用程序,导致现有 FaaS 平台与科学界之间存在重大差距。这种差距在环境科学领域尤其明显,其中 R 是重点编程语言。这是因为:1)FaaS 平台没有对 R 语言的原生支持,2)每个 FaaS 平台都有独特的界面来部署和管理由多个功能组成的工作流,从而为用户开发和部署应用程序设置了障碍。一个或多个 FaaS 平台。该项目通过开发开源软件来弥合这一差距,以加速科学应用中事件驱动的 FaaS 工作流程的采用。 FaaSr 软件将作为易于安装的 R 包进行分发,并向程序员提供简单的界面,同时支持多种开源和商业云计算基础设施。该软件将支持广泛的科学计算应用,特别是环境科学子领域(包括生态学和生物多样性)中需要动态事件驱动处理(例如预测和连续数据质量)的应用。最终,该项目旨在开发可扩展、可推广和强大的工作流程,以提高生态预测的能力、实践和培训机会,生态预测是一个活跃的科学研究领域,旨在显着提高有效环境决策和管理的预测能力。该项目开发的FaaSr软件将极大地扩展FaaS云计算基础设施的采用。目前,在用 R 语言编写的科学应用程序能够充分发挥 FaaS 平台的潜力之前,还需要克服重大挑战,因为 R 本身不支持,而且不同平台具有不同的、不兼容的编程接口。此外,科学应用程序需要由多个功能组成的工作流程,这些功能动态执行并通过在云存储中将数据作为文件交换来进行通信。不同的 FaaS 平台具有不同的编程接口来完成这些功能,从而导致开发人员和用户的复杂性增加。这个跨学科的协作项目通过将分布式系统、生态学和预测方面的专业知识集成在一起来设计和实现以下软件,从而克服了这些挑战: 由科学计算用例驱动;创建易于使用的界面;并建立在最先进的分布式计算技术和框架之上。具体来说,FaaSr软件将做出多项新颖的技术贡献,包括:1)它将允许最终用户使用R语言在高抽象级别上对工作流程进行编程; 2) 它将包括一个统一、易于使用的接口,用于处理事件调用和参数解析,向开发人员隐藏多个 FaaS 接口编程的复杂性,同时支持多个 FaaS 框架,包括 GitHub Actions、OpenWhisk、IBM Cloud Functions、和亚马逊网络服务 Lambda; 3)它将包括一个易于使用的界面,用于处理云数据存储和访问,使用事实上的标准界面和文件格式隐藏低级细节(例如,访问端点和凭证); 4)它将实现一种统一的方法来组成有向无环图工作流程,该工作流程可以自动映射到不同FaaS平台支持的编程接口。 FaaSr 的设计、实施和部署经验将为分布式/云计算贡献新技术,其中对湖泊和水库的研究作为该项目的一部分,为评估软件的性能、可扩展性和可用性提供了现实的测试平台。此外,该团队将建立并扩展其现有的本科生和研究生跨学科研究交流计划,在计算机科学、淡水科学和生态系统建模的交叉领域提供新颖的培训。该奖项由美国国家科学基金会高级网络基础设施办公室颁发由 NSF 生物科学理事会联合支持。该奖项反映了 NSF 的法定使命,并通过使用基金会的智力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

期刊论文数量(0)
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Renato Figueiredo其他文献

A Pipeline for Deep Learning with Specimen Images in iDigBio - Applying and Generalizing an Examination of Mercury Use in Preparing Herbarium Specimens
iDigBio 中标本图像深度学习的流程 - 应用和推广汞在制备植物标本室标本中的使用检查
Proceedings of the 3rd international workshop on Virtualization technologies in distributed computing
第三届分布式计算虚拟化技术国际研讨会论文集
  • DOI:
  • 发表时间:
    2009-06-15
  • 期刊:
  • 影响因子:
    0
  • 作者:
    K. Keahey;Renato Figueiredo
  • 通讯作者:
    Renato Figueiredo
Send: a social network friendship enhanced decentralized system to circumvent censorships
发送:社交网络友谊增强的去中心化系统,可规避审查
  • DOI:
  • 发表时间:
    2019
  • 期刊:
  • 影响因子:
    8.1
  • 作者:
    Ding Ding;Kyuho Jeong;Shuning Xing;Mauro Conti;Renato Figueiredo;Fangai Liu
  • 通讯作者:
    Fangai Liu
IPOP Overlay Networks for Data Sharing and Virtual Clusters in PRAGMA
用于 PRAGMA 中数据共享和虚拟集群的 IPOP 覆盖网络
  • DOI:
  • 发表时间:
    2015
  • 期刊:
  • 影响因子:
    0
  • 作者:
    Renato Figueiredo; Ken Subratie; Kyuho Jeong; Saumitra Aditya; Kohei Ichikawa
  • 通讯作者:
    Kohei Ichikawa
Science Clouds: Early Experiences in Cloud Computing for Scientific Applications
科学云:科学应用云计算的早期经验
  • DOI:
  • 发表时间:
    2024-09-13
  • 期刊:
  • 影响因子:
    0
  • 作者:
    K. Keahey;Renato Figueiredo;Jose Fortes;Tim Freeman;Maurício O. Tsugawa
  • 通讯作者:
    Maurício O. Tsugawa

Renato Figueiredo的其他文献

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{{ truncateString('Renato Figueiredo', 18)}}的其他基金

Collaborative Research: URoL:ASC: Applying rules of life to forecast emergent behavior of phytoplankton and advance water quality management
合作研究:URoL:ASC:应用生命规则预测浮游植物的紧急行为并推进水质管理
  • 批准号:
    2318862
  • 财政年份:
    2023
  • 资助金额:
    $ 50万
  • 项目类别:
    Standard Grant
I-Corps: Software-Defined Overlay Virtual Private Network for Edge Computing
I-Corps:用于边缘计算的软件定义的覆盖虚拟专用网络
  • 批准号:
    2134548
  • 财政年份:
    2021
  • 资助金额:
    $ 50万
  • 项目类别:
    Standard Grant
Collaborative Research: CIBR: Cyberinfrastructure Enabling End-to-End Workflows for Aquatic Ecosystem Forecasting
合作研究:CIBR:网络基础设施支持水生生态系统预测的端到端工作流程
  • 批准号:
    1933102
  • 财政年份:
    2020
  • 资助金额:
    $ 50万
  • 项目类别:
    Standard Grant
Collaborative Research: Elements: EdgeVPN: Seamless Secure Virtual Networking for Edge and Fog Computing
协作研究:要素:EdgeVPN:用于边缘和雾计算的无缝安全虚拟网络
  • 批准号:
    2004441
  • 财政年份:
    2020
  • 资助金额:
    $ 50万
  • 项目类别:
    Standard Grant
SaTC: CORE: Small: GOALI: Predicting and Labeling Email Phishing from Social Influence Cues and User Characteristics.
SaTC:核心:小:GOALI:根据社会影响线索和用户特征预测和标记电子邮件网络钓鱼。
  • 批准号:
    2028734
  • 财政年份:
    2020
  • 资助金额:
    $ 50万
  • 项目类别:
    Standard Grant
SaTC: CORE: Medium: Collaborative: REVELARE: A Hardware-Supported Dynamic Information Flow Tracking Framework for IoT Security and Forensics
SaTC:核心:媒介:协作:REVELARE:用于物联网安全和取证的硬件支持的动态信息流跟踪框架
  • 批准号:
    1801599
  • 财政年份:
    2018
  • 资助金额:
    $ 50万
  • 项目类别:
    Standard Grant
SaTC: CORE: Small: FIRMA: Personalized Cross-Layer Continuous Authentication
SaTC:核心:小型:FIRMA:个性化跨层连续身份验证
  • 批准号:
    1814557
  • 财政年份:
    2018
  • 资助金额:
    $ 50万
  • 项目类别:
    Standard Grant
NeTS: Small: PerSoNet: Overlay Virtual Private Networks Spanning Personal Clouds and Social Peers
NetS:小型:PerSoNet:跨越个人云和社交对等的覆盖虚拟专用网络
  • 批准号:
    1527415
  • 财政年份:
    2015
  • 资助金额:
    $ 50万
  • 项目类别:
    Standard Grant
Student Travel Support for ACM HPDC 2013
ACM HPDC 2013 学生旅行支持
  • 批准号:
    1333443
  • 财政年份:
    2013
  • 资助金额:
    $ 50万
  • 项目类别:
    Standard Grant
SHF: Small: Collaborative Research: Exploring Energy-Efficient GPGPUs Through Emerging Technology Integration
SHF:小型:协作研究:通过新兴技术集成探索节能 GPGPU
  • 批准号:
    1320100
  • 财政年份:
    2013
  • 资助金额:
    $ 50万
  • 项目类别:
    Standard Grant

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