| NSF Org: |
CNS Division Of Computer and Network Systems |
| Recipient: |
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| Initial Amendment Date: | August 4, 2018 |
| Latest Amendment Date: | April 13, 2020 |
| Award Number: | 1763834 |
| Award Instrument: | Standard Grant |
| Program Manager: |
Erik Brunvand
CNS Division Of Computer and Network Systems CSE Directorate for Computer and Information Science and Engineering |
| Start Date: | October 1, 2018 |
| End Date: | September 30, 2021 (Estimated) |
| Total Intended Award Amount: | $119,189.00 |
| Total Awarded Amount to Date: | $135,189.00 |
| Funds Obligated to Date: |
FY 2019 = $8,000.00 FY 2020 = $8,000.00 |
| History of Investigator: |
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| Recipient Sponsored Research Office: |
101 COMMONWEALTH AVE AMHERST MA US 01003-9252 (413)545-0698 |
| Sponsor Congressional District: |
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| Primary Place of Performance: |
MA US 01003-9264 |
| 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): |
CSR-Computer Systems Research, Special Projects - CNS |
| Primary Program Source: |
01001920DB NSF RESEARCH & RELATED ACTIVIT 01001819DB NSF RESEARCH & RELATED ACTIVIT |
| 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
Smart wearables, the Internet of Things, and new application types, such as augmented reality promise to revolutionize how people interact with technology in their daily lives. While embedded and smart devices have growing capabilities, they still rely on a backend cloud infrastructure to provide additional storage and computational capacity. However, these new application types have characteristics such as strict performance requirements and frequent mobility that are ill suited for today's centralized clouds. This project will develop new system architectures that will increase the scalability, elasticity, and mobility of "edge" applications that connect to mobile users.
Towards this end, the project will explore the communication and system architectures needed to effectively support edge cloud services. The project will leverage advances in network function virtualization to provide high performance networking, and will explore the communication and Operating System primitives needed to support scalable middleboxes and application endpoints. Using this platform as a base, the project will design models that capture the new challenges inherent in mobile edge cloud workloads. These models will be used to guide elastic scaling algorithms.
We are increasingly reliant on mobile computing devices to guide our cars, help us keep in touch with others, gather data of our surroundings, and more. The mobile elastic edge cloud platform being developed in this project will help improve the scalability, agility, and efficiency of edge clouds, allowing them to support new types of performance critical applications. The researchers will engage a broad range of students from the undergraduate to Ph.D. levels in the educational and research activities of this grant.
There will be a project website (http://faculty.cs.gwu.edu/timwood/projects/me2c) that includes all of the artifacts produced throughout the project as well as links to key related technologies and papers. The web repository will include all of the source code developed during the course of the project, documentation with guidance to adopters on using the software, and links to all the papers published and technical reports that are released publicly. The project web page will be maintained for a period of five years after the end of the project.
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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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.
This project led to new methods to model mobility of human users using network logs of mobile devices, which led to new insights for mobile edge cloud workloads. The research showed that mobile device behavior should not treated as independent, and it is correlated other devices owned by that user. In addition, device mobility behavior is correlated to group interactions of a user and is correlated to the mobile devices belonging to other users. The project also led to new insights for running AI workloads on edge servers. It showed that AI workloads running on edge servers with neural accelerators can see interference from other co-located applications and pointed to the need for better performance isolation mechanisms for edge servers.
Finally, the work on understanding mobility of users and their devices led to the design of a WiFi-based contact tracing tool that uses device mobility to infer close contacts of an infected user. The tool was released as open source and used by multiple universities for pilot studies of contact tracing during the covid-19 pandemic.
The project trained one female Ph.D student, who graduated and went on to pursue her postdoctoral research. The project also supported a female REU student, who gained exposure to mobile edge computing and the Internet of Things. Two other students received partial training on the project. The students supported on this grant also contributed to K-12 outreach through the UMass Turing Institute, which provides summer outreach to local high school students.
Last Modified: 01/29/2022
Modified by: Prashant Shenoy
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