Award Abstract # 2018472
CC* Integration-Small: Integrating Application Agnostic Learning with FABRIC for Enabling Realistic High-Fidelity Traffic Generation and Modeling

NSF Org: OAC
Office of Advanced Cyberinfrastructure (OAC)
Recipient: UNIVERSITY OF HOUSTON SYSTEM
Initial Amendment Date: July 1, 2020
Latest Amendment Date: July 18, 2023
Award Number: 2018472
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, 2020
End Date: February 29, 2024 (Estimated)
Total Intended Award Amount: $299,956.00
Total Awarded Amount to Date: $359,946.00
Funds Obligated to Date: FY 2020 = $299,956.00
FY 2023 = $59,990.00
History of Investigator:
  • Deniz Gurkan (Principal Investigator)
    dgurkan@kent.edu
Recipient Sponsored Research Office: University of Houston
4300 MARTIN LUTHER KING BLVD
HOUSTON
TX  US  77204-3067
(713)743-5773
Sponsor Congressional District: 18
Primary Place of Performance: University of Houston
4800 Calhoun Boulevard
Houston
TX  US  77204-2015
Primary Place of Performance
Congressional District:
18
Unique Entity Identifier (UEI): QKWEF8XLMTT3
Parent UEI:
NSF Program(s): CISE Research Resources
Primary Program Source: 01002324DB NSF RESEARCH & RELATED ACTIVIT
01002021DB NSF RESEARCH & RELATED ACTIVIT
Program Reference Code(s): 9102
Program Element Code(s): 289000
Award Agency Code: 4900
Fund Agency Code: 4900
Assistance Listing Number(s): 47.070

ABSTRACT

Novel approaches to networking and application development require high-fidelity testing and evaluation supported by realistic network usage scenarios. Furthering the pursuit of these novel approaches, the FABRIC testbed (https://whatisfabric.net) can store and process information "in the network" in ways not possible in the current Internet, which will lead to completely new networking protocols, architectures and applications that address pressing problems with performance, security and adaptability in the Internet. This project will provide researchers the means to easily utilize the new capabilities of the FABRIC testbed through a suite of new tools - smoothing the transition of existing experiments to the testbed and enabling exciting new areas of research.

This project will produce three systems facilitating end to end traffic modeling and generation in the FABRIC environment. A model repository will be created for the storage and access of custom models by experimenters, and will be seeded with stock models of some popular applications for immediate use. The use of the models within FABRIC-hosted experiments will be advanced through a bespoke matching system that will align experiment resources with model requirements. Finally, for experiments developing novel applications, a tool will be provided for creating new models using data captured with FABRIC infrastructure components.

FABRIC users will gain direct low-friction access to the novel infrastructure capabilities of the testbed, freeing them to focus the bulk of their time and effort on their own research goals rather than dealing with the vagaries of resource availability, specialized driver setup, and complex data formats. As a result, testbed resources can be more optimally shared between experiments, and individual research tasks will be completed more quickly. The project will also provide input to future researchers and testbed implementors on streamlining workflows of high level services in support of research objectives over advanced testbed technologies.

Documentation for project tools and code, as well as backing project data, will be located at http://docs.uh-netlab.org, and it will be publicly available for at least 5 years after the end of substantive project work. In-development source code is available on an ongoing basis via public internet resources linked from the documentation site.

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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Baxley, Stuart and Gurkan, Deniz and Validi, Hamidreza and Hicks, Illya "Graph Representation of Computer Network Resources for Precise Allocations" ICCCN , 2022 https://doi.org/10.1109/ICCCN54977.2022.9868852 Citation Details

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