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Research Spending & Results

Award Detail

Doing Business As Name:College of William and Mary
  • Xu Liu
  • (757) 221-7739
Award Date:07/10/2020
Estimated Total Award Amount: $ 249,840
Funds Obligated to Date: $ 249,840
  • FY 2020=$249,840
Start Date:10/01/2020
End Date:09/30/2023
Transaction Type:Grant
Awarding Agency Code:4900
Funding Agency Code:4900
CFDA Number:47.070
Primary Program Source:040100 NSF RESEARCH & RELATED ACTIVIT
Award Title or Description:Collaborative Research:CNS Core:Small:Towards Efficient Cloud Services
Federal Award ID Number:2007922
DUNS ID:074762238
Parent DUNS ID:074762238
Program:CSR-Computer Systems Research
Program Officer:
  • Matt Mutka
  • (703) 292-7344

Awardee Location

Street:Office of Sponsored Programs
Awardee Cong. District:02

Primary Place of Performance

Organization Name:College of William and Mary
Street:251 Jamestown Rd.
Cong. District:02

Abstract at Time of Award

Cloud computing frameworks enable a wide range of services while sharing computation resources and infrastructure costs. To achieve these benefits, cloud computing frameworks rely on layers of abstractions to reduce the complexity of distributed and heterogeneous computational infrastructure. Abstractions hide resource management complexities and improve programmability. However, abstractions make cloud frameworks less observable, resulting in various forms of inefficiencies. This project will address the challenges of practical cloud monitoring techniques to guide cloud application development and system design. This project will explore the inefficiency patterns in cloud computing infrastructures and applications. More specifically, it will provide novel measurement techniques to enable monitoring these inefficiencies across the cloud layers of abstraction. Additionally, the project will develop tools that will provide actionable insights for high-performance cloud frameworks and application development. This project has three thrusts. First, it will measure language-level abstractions for intra-application inefficiencies. Second, it will explore the inefficient communication patterns among microservices for inter-service optimization. Third, it will develop tools to analyze inefficiencies in the entire stack of cloud software layers of abstraction. This project will bridge the knowledge gap between application developers and system designers to provide more efficient cloud environments. It will advance the state-of-the-art cloud monitoring techniques and address the current and future challenges in the cloud computing community. The tools developed from this project will have broad interest from industry, research institutes, and laboratories for efficient code execution and high system throughput. Furthermore, the project will disseminate the obtained knowledge through hands-on training sessions and tutorials. Finally, the project will facilitate curriculum development with a particular focus on involving minority and under-represented students. The project will maintain a website at The website will host all the project outcomes, including the publications, open-source code, toolkits, datasets, documentation, and tutorials. The website will be accessible to the public throughout the project lifetime and beyond. 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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