CNS Core: Small: Exploring the Benefits on Non-Work-Conserving Networking
- Funded by National Science Foundation (NSF)
- Total publications:0 publications
Grant number: unknown
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Key facts
Disease
COVID-19Start & end year
20212023Known Financial Commitments (USD)
$458,591Funder
National Science Foundation (NSF)Principal Investigator
Roch GuerinResearch Location
United States of AmericaLead Research Institution
Washington UniversityResearch Priority Alignment
N/A
Research Category
Secondary impacts of disease, response & control measures
Research Subcategory
Other secondary impacts
Special Interest Tags
N/A
Study Type
Non-Clinical
Clinical Trial Details
N/A
Broad Policy Alignment
Pending
Age Group
Not Applicable
Vulnerable Population
Not applicable
Occupations of Interest
Not applicable
Abstract
Metering gates that pace traffic at highway entrances during peak hours have become common practice. They prevent multiple cars from entering back-to-back, which can create bubbles of congestion that propagate and generate broad disruptions. A similar principle applies in the data networks that make up the modern communication infrastructure. This project explores the use of such metering/pacing options to improve the performance of those networks. In particular, can latency-sensitive traffic such as interactive transactions, games, audio and video, etc., be paced, i.e., slowed down on entry, while improving the network's ability to deliver it in time? Answering this question is the goal of this project.
Most end-points feeding the Internet are today located in datacenters overseen by large-scale providers. This offers the opportunity for better control of the traffic they generate. This project aims to develop a principled approach for selecting how to best pace end-point traffic as a function of traffic profiles and sensitivity to delay towards minimizing the network resources (and therefore cost) required. This can be formulated as an optimization problem whose complexity, however, grows rapidly with network size. The project targets practical guidelines to realize near-optimal pacing together with software solutions to orchestrate the resulting controls, and an evaluation of the benefits they afford.
The recent COVID-19 crisis and the shift to working remotely and to online learning has triggered an unprecedented growth in the volume of "real-time" traffic. This has in turn stressed the network infrastructure responsible for carrying it, e.g., forcing some providers to limit the amount of traffic they allow. The approaches this project seeks to develop have the potential to allow those same networks to carry substantially more real-time traffic while preserving performance and without requiring costly upgrades. The work will be carried out in collaboration with a large-scale cloud provider to ensure it captures the practical constraints that networks connecting cloud sites face.
The project's website is in the form of a wiki hosted by the Open Science Foundation and accessible at https://osf.io/mh4eg/. The wiki serves as an entry point for results produced by the project. It includes a section devoted to publications produced under the project's auspices and setup to ensure open-access to those works. Other sections will provide either repositories or links to external repositories for data sets and software produced as part of the project. Repositories will be selected based on their accessibility and ability to ensure long-term access to hosted information. Selected repositories will ensure preservation of the data for at least five years beyond the project's end, and preferably longer.
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.
Most end-points feeding the Internet are today located in datacenters overseen by large-scale providers. This offers the opportunity for better control of the traffic they generate. This project aims to develop a principled approach for selecting how to best pace end-point traffic as a function of traffic profiles and sensitivity to delay towards minimizing the network resources (and therefore cost) required. This can be formulated as an optimization problem whose complexity, however, grows rapidly with network size. The project targets practical guidelines to realize near-optimal pacing together with software solutions to orchestrate the resulting controls, and an evaluation of the benefits they afford.
The recent COVID-19 crisis and the shift to working remotely and to online learning has triggered an unprecedented growth in the volume of "real-time" traffic. This has in turn stressed the network infrastructure responsible for carrying it, e.g., forcing some providers to limit the amount of traffic they allow. The approaches this project seeks to develop have the potential to allow those same networks to carry substantially more real-time traffic while preserving performance and without requiring costly upgrades. The work will be carried out in collaboration with a large-scale cloud provider to ensure it captures the practical constraints that networks connecting cloud sites face.
The project's website is in the form of a wiki hosted by the Open Science Foundation and accessible at https://osf.io/mh4eg/. The wiki serves as an entry point for results produced by the project. It includes a section devoted to publications produced under the project's auspices and setup to ensure open-access to those works. Other sections will provide either repositories or links to external repositories for data sets and software produced as part of the project. Repositories will be selected based on their accessibility and ability to ensure long-term access to hosted information. Selected repositories will ensure preservation of the data for at least five years beyond the project's end, and preferably longer.
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.