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

• 批准号: 2126281
• 负责人: Ramakrishnan Durairajan
• 金额: $ 98.85万
• 依托单位: University of Oregon Eugene
• 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
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2126281&HistoricalAwards=false
• 关键词: CC; Integration; Large; Bringing; Code

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.

成功应用机器学习（ML）在网络问题上的应用取决于来自现实世界网络的高质量标记数据的可用性。同样至关重要的是共享这些数据集的能力，尊重数据所有者的隐私问题。不幸的是，缺少通过当今常用的数据对代码范式共享数据，研究人员缺乏与第三方收集和策划的数据合作或受益的系统框架。 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标记和知识共享框架分为三个推力。首先，该项目将调查一种新颖的代码到数据方法，该方法将组织共享操作员域知识的程序化表示形式，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，以使互联网数据科学成为全面的协作工作。第三，该项目将在两所参与大学（UO和UCSB）之间的合作努力下说明拟议框架的能力，并证明其扩展到任何数量的参与者的能力。结果框架将成为促进互联网数据科学新兴领域的协作努力的驱动力。除了确定有关如何在网络中使用ML的一些根本变化外，研究结果还将受益于行业和学术界，并确保明天的劳动力对适当的培训进行了适当的培训，以充分利用ML在特定网络特定问题中的应用。 Also, the outcomes will catalyze the development of a roadmap for the adoption of Internet Data Science efforts by operators and the deployment of ensuring 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 precious of support through evaluation利用基金会的知识分子和更广泛的影响审查标准。

项目成果

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: ACM
• 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

• 期刊: IEEE/ACM Transactions on Networking
• 作者: Chris Misa;Ramakrishnan Durairajan;R. Rejaie

Estimating WebRTC Video QoE Metrics Without Using Application Headers

• DOI: 10.1145/3618257.3624828
• 发表时间: 2023-06
• 期刊: Proceedings of the 2023 ACM on Internet Measurement Conference
• 作者: Taveesh Sharma;Tarun Mangla;Arpit Gupta;Junchen Jiang;N. Feamster

Dynamic Scheduling of Approximate Telemetry Queries

• 发表时间: 2022
• 作者: Chris Misa;Walt O'Connor;Ramakrishnan Durairajan;R. Rejaie;Walter Willinger

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: ACM SIGMETRICS Performance Evaluation Review
• 作者: Willinger, Walter;Gupta, Arpit;Jacobs, Arthur S.;Beltiukov, Roman;Ferreira, Ronaldo A.;Granville, Lisandro
• 通讯作者: Granville, Lisandro