Area:
Functional organization, Somatotopy, Primary motor area, Premotor cortex, Supplementary motor area, Inferior parietal area, Motor intention, Motor preparation
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High-probability grants
According to our matching algorithm, Zheng Song is the likely recipient of the following grants.
| Years |
Recipients |
Code |
Title / Keywords |
Matching
score |
| 2021 — 2023 |
Song, Zheng |
N/AActivity Code Description:
No activity code was retrieved: click on the grant title for more information |
Crii: Cns: With Uncertainty Comes Opportunity: Providing Best-Effort Edge Services With Uncertain and Limited Resources @ Regents of the University of Michigan - Dearborn
The goal of this project is to create a runtime system for providing best-effort edge services. Different from clouds with manageable and redundant resources, many edge environments are opportunistic and feature uncertain and limited resources. As a result, edge services cannot guarantee Quality of Service (QoS) for their clients, which is a critical obstacle of creating current and emerging edge computing applications. Existing solutions fall short, as they optimize the overall QoS by allocating bottleneck resources competed for by services. In contrast, this project is based on the insight that the uncertainty of edge environments also brings opportunity so the equivalent functions that do not consume the same resources can satisfy the same service request. Hence, this project will explore how to systematically improve QoS by dynamically orchestrating equivalent functions that consume underutilized resources.
The technologies developed during the course of this project will accelerate large-scale applications of edge computing in emerging domains like autonomous vehicles, smart homes, and Industry 4.0. A particular objective is to make it possible to use any kinds of hardware devices in edge computing environments. The software infrastructure developed for this project will be used for instructional purposes to create new and enrich existing courses on distributed systems and edge computing. Executed in the Detroit Metropolitan area, this project provides an excellent opportunity for outreach to local diverse communities, thus accelerating end-user adoption of edge computing technologies and attracting underrepresented groups into computing study and research.
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.
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