CC* Integration-Large: Bringing Code to Data: A Collaborative Approach to Democratizing Internet Data Science

CC* Integration-Large:将代码带入数据:互联网数据科学民主化的协作方法

基本信息

  • 批准号:
    2126281
  • 负责人:
  • 金额:
    $ 98.85万
  • 依托单位:
  • 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:
    2021-10-01 至 2024-09-30
  • 项目状态:
    已结题

项目摘要

Successful application of machine learning (ML) for networking problems depends on the availability of high-quality labeled data from real-world networks. Equally critical is the ability to share these datasets, respecting the data owners' privacy concerns. Unfortunately, short of sharing the data via today’s commonly-applied data-to-code paradigm, researchers lack a systematic framework for working with or benefiting from data collected and curated by third parties. Consequently, Internet Data Science as practiced today is ill-suited for applications such as (i) high-quality data labeling, (ii) rigorous evaluation of research artifacts such as learning models, and (iii) independent validation/reproducibility of reported research findings.This collaborative project brings together researchers from University of Oregon, University of California-Santa Barbara, and NIKSUN, Inc., and will investigate an innovative collaborative data labeling and knowledge sharing framework in three thrusts. First, the project will investigate a novel code-to-data approach that entails sharing of programmatic representations of operators' domain knowledge to identify events of interest in the data. Second, the project will design and develop a new learning framework to enable the pursuit of Internet Data Science as a full-fledged collaborative effort. Third, the project will illustrate the capabilities of the proposed framework in the context of collaborative efforts between two participating universities (UO and UCSB) and demonstrate its ability to scale to any number of participants.The resulting framework will serve as a driving force for advancing collaborative efforts in the emerging area of Internet Data Science. In addition to identifying some of the fundamental changes to how ML ought to be used in networking, the research findings will benefit both industry and academia and will ensure that tomorrow's workforce has the proper training to fully exploit the application of ML for network-specific problems. Also, the outcomes will catalyze the development of a roadmap for the adoption of Internet Data Science efforts by operators and the deployment of ensuing research artifacts in real-world production networks.This project will maintain the following webpage: https://onrg.gitlab.io/projects/emerge.html.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.
机器学习(ML)在网络问题中的成功应用取决于来自真实网络的高质量标记数据的可用性。同样重要的是能够共享这些数据集,尊重数据所有者的隐私问题。不幸的是,由于缺乏通过当今普遍应用的数据到代码范式共享数据,研究人员缺乏一个系统的框架来使用或受益于第三方收集和管理的数据。因此,今天实践的互联网数据科学不适合应用,例如(i)高质量的数据标记,(ii)对研究工件(如学习模型)的严格评估,以及(iii)对报告的研究结果的独立验证/再现。这个合作项目汇集了来自俄勒冈州大学、加利福尼亚大学圣巴巴拉分校和NIKSUN公司的研究人员,并将在三个方面研究创新的协作数据标签和知识共享框架。首先,该项目将研究一种新的代码到数据的方法,该方法需要共享运营商领域知识的编程表示,以识别数据中感兴趣的事件。其次,该项目将设计和开发一个新的学习框架,使互联网数据科学成为一个全面的合作努力。第三,该项目将在两所参与大学(UO和UCSB)之间的合作努力的背景下说明拟议框架的能力,并展示其扩展到任何数量的参与者的能力。由此产生的框架将成为推动互联网数据科学新兴领域合作努力的驱动力。除了确定如何在网络中使用ML的一些根本性变化外,研究结果将使工业界和学术界受益,并将确保未来的劳动力得到适当的培训,以充分利用ML的应用程序解决网络特定的问题。此外,这些成果还将促进运营商采用互联网数据科学成果的路线图的制定,并将随后的研究成果部署到现实世界的生产网络中。该项目将维护以下网页:https://onrg.gitlab.io/projects/emerge.html.This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

期刊论文数量(10)
专著数量(0)
科研奖励数量(0)
会议论文数量(0)
专利数量(0)
PINOT: Programmable Infrastructure for Networking
PINOT:可编程网络基础设施
  • DOI:
    10.1145/3606464.3606485
  • 发表时间:
    2023
  • 期刊:
  • 影响因子:
    0
  • 作者:
    Beltiukov, Roman;Chandrasekaran, Sanjay;Gupta, Arpit;Willinger, Walter
  • 通讯作者:
    Willinger, Walter
DynATOS+: A Network Telemetry System for Dynamic Traffic and Query Workloads
  • DOI:
  • 发表时间:
  • 期刊:
  • 影响因子:
    0
  • 作者:
    Chris Misa;Ramakrishnan Durairajan;R. Rejaie
  • 通讯作者:
    Chris Misa;Ramakrishnan Durairajan;R. Rejaie
Estimating WebRTC Video QoE Metrics Without Using Application Headers
A NetAI Manifesto (Part I): Less Explorimentation, More Science
NetAI 宣言(第一部分):更少的探索,更多的科学
  • DOI:
    10.1145/3626570.3626609
  • 发表时间:
    2023
  • 期刊:
  • 影响因子:
    0
  • 作者:
    Willinger, Walter;Gupta, Arpit;Jacobs, Arthur S.;Beltiukov, Roman;Ferreira, Ronaldo A.;Granville, Lisandro
  • 通讯作者:
    Granville, Lisandro
Dynamic Scheduling of Approximate Telemetry Queries
  • DOI:
  • 发表时间:
    2022
  • 期刊:
  • 影响因子:
    0
  • 作者:
    Chris Misa;Walt O'Connor;Ramakrishnan Durairajan;R. Rejaie;Walter Willinger
  • 通讯作者:
    Chris Misa;Walt O'Connor;Ramakrishnan Durairajan;R. Rejaie;Walter Willinger
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Ramakrishnan Durairajan其他文献

A Techno-Economic Framework for Broadband Deployment in Underserved Areas
服务欠缺地区宽带部署的技术经济框架
  • DOI:
    10.1145/2940157.2940159
  • 发表时间:
    2016
  • 期刊:
  • 影响因子:
    4.5
  • 作者:
    Ramakrishnan Durairajan;P. Barford
  • 通讯作者:
    P. Barford
Internet atlas: a geographic database of the internet
互联网地图集:互联网地理数据库
  • DOI:
    10.1145/2491159.2491170
  • 发表时间:
    2013
  • 期刊:
  • 影响因子:
    0
  • 作者:
    Ramakrishnan Durairajan;Subhadip Ghosh;Xin Tang;P. Barford;Brian Eriksson
  • 通讯作者:
    Brian Eriksson
On the Resilience of Internet Infrastructures in Pacific Northwest to Earthquakes
西北太平洋地区互联网基础设施的抗震能力
Automatic metadata generation for active measurement
自动生成元数据以进行主动测量
InterTubes: A Study of the US Long-haul Fiber-optic Infrastructure
InterTubes:美国长途光纤基础设施研究

Ramakrishnan Durairajan的其他文献

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{{ truncateString('Ramakrishnan Durairajan', 18)}}的其他基金

Collaborative Research: SaTC: CORE: Medium: ONSET: Optics- enabled Network Defenses for Extreme Terabit DDoS Attacks
协作研究:SaTC:核心:中:ONSET:针对极端太比特 DDoS 攻击的光学网络防御
  • 批准号:
    2132651
  • 财政年份:
    2022
  • 资助金额:
    $ 98.85万
  • 项目类别:
    Standard Grant
CAREER: Argus: A Measurement-informed Learning Approach to Managing Multi-cloud Networks
职业:Argus:管理多云网络的基于测量的学习方法
  • 批准号:
    2145813
  • 财政年份:
    2022
  • 资助金额:
    $ 98.85万
  • 项目类别:
    Continuing Grant
CRII: NeTS: Denoising Internet Delay Measurements using Weak Supervision
CRII:NeTS:使用弱监督对互联网延迟测量进行去噪
  • 批准号:
    1850297
  • 财政年份:
    2019
  • 资助金额:
    $ 98.85万
  • 项目类别:
    Standard Grant

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