Award Abstract # 2126281
CC* Integration-Large: Bringing Code to Data: A Collaborative Approach to Democratizing Internet Data Science

NSF Org: OAC
Office of Advanced Cyberinfrastructure (OAC)
Recipient: UNIVERSITY OF OREGON
Initial Amendment Date: September 16, 2021
Latest Amendment Date: March 28, 2023
Award Number: 2126281
Award Instrument: Standard Grant
Program Manager: Deepankar Medhi
dmedhi@nsf.gov
 (703)292-2935
OAC
 Office of Advanced Cyberinfrastructure (OAC)
CSE
 Directorate for Computer and Information Science and Engineering
Start Date: October 1, 2021
End Date: September 30, 2025 (Estimated)
Total Intended Award Amount: $988,548.00
Total Awarded Amount to Date: $988,548.00
Funds Obligated to Date: FY 2021 = $988,548.00
History of Investigator:
  • Ramakrishnan Durairajan (Principal Investigator)
    ram@cs.uoregon.edu
  • Reza Rejaie (Co-Principal Investigator)
  • Arpit Gupta (Co-Principal Investigator)
  • Jon Miyake (Co-Principal Investigator)
  • David Teach (Former Co-Principal Investigator)
Recipient Sponsored Research Office: University of Oregon Eugene
1776 E 13TH AVE
EUGENE
OR  US  97403-1905
(541)346-5131
Sponsor Congressional District: 04
Primary Place of Performance: University of Oregon Eugene
OR  US  97403-5219
Primary Place of Performance
Congressional District:
04
Unique Entity Identifier (UEI): Z3FGN9MF92U2
Parent UEI: Z3FGN9MF92U2
NSF Program(s): CISE Research Resources
Primary Program Source: 01002122DB NSF RESEARCH & RELATED ACTIVIT
Program Reference Code(s):
Program Element Code(s): 289000
Award Agency Code: 4900
Fund Agency Code: 4900
Assistance Listing Number(s): 47.070

ABSTRACT

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.

PUBLICATIONS PRODUCED AS A RESULT OF THIS RESEARCH

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(Showing: 1 - 10 of 14)
Khan, Punnal Ismail and Guthula, Satyandra and Beltiukov, Roman and Schmid, Roland and Bühler, Tobias and Gupta, Arpit and Vanbever, Laurent and Willinger, Walter "Harnessing Public Code Repositories to Develop Production-Ready ML Artifacts for Networking" , 2024 https://doi.org/10.1145/3673422.3674898 Citation Details
Beltiukov, Roman and Chandrasekaran, Sanjay and Gupta, Arpit and Willinger, Walter "PINOT: Programmable Infrastructure for Networking" , 2023 https://doi.org/10.1145/3606464.3606485 Citation Details
Beltiukov, Roman and Guo, Wenbo and Gupta, Arpit and Willinger, Walter "In Search of netUnicorn: A Data-Collection Platform to Develop Generalizable ML Models for Network Security Problems" , 2023 https://doi.org/10.1145/3576915.3623075 Citation Details
Chris, Misa and OConnor, Walt and Durairajan, Ramakrishnan and Rejaie, Reza and Willinger, Walter "Dynamic Scheduling of Approximate Telemetry Queries" NSDI , 2022 Citation Details
Jacobs, Arthur and Beltiukov, Roman and Willinger, Walter and Ferreira, Ronaldo and Gupta, arpit and Granville Lisandro "AI/ML for Network Security: The Emperor has no Clothes" ACM Conference on Computer and Communications Security (CCS) , 2022 Citation Details
Knofczynski, Jared and Durairajan, Ramakrishnan and Willinger, Walter "ARISE: A Multitask Weak Supervision Framework for Network Measurements" IEEE Journal on Selected Areas in Communications , v.40 , 2022 https://doi.org/10.1109/JSAC.2022.3180783 Citation Details
Misa, Chris and Durairajan, Ramakrishnan and Gupta, Arpit and Rejaie, Reza and Willinger, Walter "Leveraging Prefix Structure to Detect Volumetric DDoS Attack Signatures with Programmable Switches" , 2024 https://doi.org/10.1109/SP54263.2024.00267 Citation Details
Misa, Chris and Durairajan, Ramakrishnan and Rejaie, Reza and Willinger, Walter "DynATOS+: A Network Telemetry System for Dynamic Traffic and Query Workloads" IEEE/ACM Transactions on Networking , 2024 Citation Details
Misa, Chris and Durairajan, Ramakrishnan and Rejaie, Reza and Willinger, Walter "DynATOS+: A Network Telemetry System for Dynamic Traffic and Query Workloads" IEEE/ACM Transactions on Networking , 2024 Citation Details
Nance-Hall, Matthew and Barford, Paul and Foerster, Klaus-Tycho and Durairajan, Ramakrishnan "Improving Scalability in Traffic Engineering via Optical Topology Programming" IEEE Transactions on Network and Service Management , 2024 https://doi.org/10.1109/TNSM.2023.3335898 Citation Details
Sharma, Taveesh and Mangla, Tarun and Gupta, Arpit and Jiang, Junchen and Feamster, Nick "Estimating WebRTC Video QoE Metrics Without Using Application Headers" , 2023 https://doi.org/10.1145/3618257.3624828 Citation Details
(Showing: 1 - 10 of 14)

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