| NSF Org: |
CNS Division Of Computer and Network Systems |
| Recipient: |
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| Initial Amendment Date: | August 5, 2016 |
| Latest Amendment Date: | August 5, 2016 |
| Award Number: | 1618207 |
| Award Instrument: | Standard Grant |
| Program Manager: |
Darleen Fisher
CNS Division Of Computer and Network Systems CSE Directorate for Computer and Information Science and Engineering |
| Start Date: | August 15, 2016 |
| End Date: | July 31, 2020 (Estimated) |
| Total Intended Award Amount: | $499,951.00 |
| Total Awarded Amount to Date: | $499,951.00 |
| Funds Obligated to Date: |
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| History of Investigator: |
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| Recipient Sponsored Research Office: |
1 SILBER WAY BOSTON MA US 02215-1703 (617)353-4365 |
| Sponsor Congressional District: |
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| Primary Place of Performance: |
Boston MA US 02215-1300 |
| Primary Place of
Performance Congressional District: |
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| Unique Entity Identifier (UEI): |
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| Parent UEI: |
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| NSF Program(s): | Networking Technology and Syst |
| Primary Program Source: |
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| Program Reference Code(s): |
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| Program Element Code(s): |
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| Award Agency Code: | 4900 |
| Fund Agency Code: | 4900 |
| Assistance Listing Number(s): | 47.070 |
ABSTRACT
The Internet is collectively composed of tens of thousands of individual networks, operated independently. Data flows from one network to another over paths that reflect agreements between network providers to exchange traffic. The ability of the Internet to connect computers around the world depends on the establishment and maintenance of these paths using a complex set of protocols. Hence, understanding the set of paths used in the Internet at any given time is important for the Internet's continued smooth operation. Unfortunately there are relatively few good metrics and algorithms for gaining insight into how the Internet's paths are currently configured and how they are changing over time.
This project is developing new methods to address the lack of good path analysis tools. Using these novel metrics and algorithms, this project's goal is to give insight into a wide range of questions, including identifying when data traffic may be being hijacked for malicious purposes, and when network operators shift their business strategies. The project is developing metrics based on Routing State Distance (RSD), and examining both micro-level questions (at the level of individual Autonomous Systems (ASes)) and macro-level questions (at the level of the whole Internet). At the micro level, the project is examining methods for identifying co-managed ASes, unusually-routed ASes, and shifts in the routing towards individual ASes. At the macro level, the project is assessing the "flattening" of the Internet and large-scale trends in the factors that drive unusual routing behavior. A key component of the project is the identification of where and how to do active measurement to most effectively obtain information about the global routing system.
Broader Impact. The methods being develop have potential to guide improvements to Internet operation, which benefits society broadly. Results from this project will be presented to network operators to help guide future understanding of Internet routing. Additional broader impacts of this project are being felt through educational development and continued support for under-represented groups. As part of this project, the PI is developing learning modules and exercises focusing on network analysis for inclusion in his course "Computer Networks" as well as contributing modules for inclusion in the course "Tools and Techniques for Data Mining" which he is helping to develop. The PI is committed to the engagement of under-represented groups and women in his research, and currently supervises two female Ph.D. students.
PUBLICATIONS PRODUCED AS A RESULT OF THIS RESEARCH
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PROJECT OUTCOMES REPORT
Disclaimer
This Project Outcomes Report for the General Public is displayed verbatim as submitted by the Principal Investigator (PI) for this award. Any opinions, findings, and conclusions or recommendations expressed in this Report are those of the PI and do not necessarily reflect the views of the National Science Foundation; NSF has not approved or endorsed its content.
The goal of this project is to better understand the "paths" of the Internet, that is, the routes that data takes when it travels from a source (like a Web server) to a destination (like a desktop computer). To this end, the project seeks to develop metrics and algorithms for gaining insight into how the Internet's paths are currently configured and how they are changing over time. A further, longer-range goal is to understand whether the tools developed are more broadly applicable to other networks, including biological and social networks.
The canonical system studied in this project is the interdomain routing system. This is the system that determines how traffic flows from one ISP (referred to as an Automous System, AS) to another. The project developed a method for detecting unusually-routed Autonomous Systems, applied the method, and showed that it identified hosts that have business reasons for adopting unusual routing structures. It also showed how to use clustering to extract ASes with common ownership or management. The project also showed how individual ASes vary their routing structure over time, and identified a number of reasons why they make signficant changes to the sets of paths reaching them. And it showed how to cluster the entire set of paths over a 10-year timespan to identify major changes in the most unusually-routed ASes. These changes correspond with significant shifts in the economic landscape of the Internet's major players.
The project results developed show general applicability, and have the potential to make a contribution to the theory of network analysis. The results bring new analytic tools in the form of new metrics for path based network analysis; new clustering problems and algorithms for solving them; and segmentation methods for analysis of outlier sets (as used in segmenting the timeline of Internet evolution).
Based on the success of these methods and metrics developed to analyze Internet routing, we showed successful results in analyzing social networks (including aspects of human mobility) as well as biological networks (identifying genes associated with Alzheimer's disease and COVID-19).
Last Modified: 12/08/2020
Modified by: Mark E Crovella
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