CC* Integration-Small: A Software-Defined Edge Infrastructure Testbed for Full-stack Data-Driven Wireless Network Applications
CC* Integration-Small: A Software-Defined Edge Infrastructure Testbed for Full-stack Data-Driven Wireless Network Applications
批准号:
2201536
负责人:
Flavio Esposito
金额:
$50.0万
依托单位:
依托单位国家:
美国
项目类别:
Standard Grant
财政年份:
2022
资助国家:
美国
项目状态:
已结题
起止时间:
2022-07-01 至 2024-06-30
中文摘要
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英文摘要
Interdisciplinary research advances often require devices to collect, process, and transfer large scientific datasets over high bandwidth links. The overarching goal of this project is to build a wireless virtual network testbed at Saint Louis University, in collaboration with Northeastern University, to evaluate network management solutions that integrate the use of machine learning and artificial intelligence with programmable radios and programmable network switches. To evaluate the proposed innovation in computer networking, the cyberinfrastructure will be used to prototype network protocols and systems in support of a few interdisciplinary initiatives on campus.In particular, this project's contributions will be developed around the integration of learning techniques with network mechanisms such as medium access control, routing, and transport services. First, the team will explore the design and implementation of effective transport and routing protocols that integrate the network stack at different scopes using recent advances in reinforcement learning. Second, novel network architectures will be proposed integrating edge network mechanisms with federated and split learning techniques. Third, cross-layer distributed learning protocols will be designed to create self-adaptive wireless networks. Such solutions will be tested on campus and on other network testbeds.By combining synergies from the fields of data science and network virtualization protocols and architectures, this work will lay the foundation for further research in adaptive resource management for (wireless) edge computing applications that can improve the quality of life in our society. This project's results will be valuable for other fields interested in real-time prediction, such as robotics, medicine, anthropology, and finance. The research in this project will be impactful also thanks to the planned industry and international collaborations. Students from underrepresented groups will be involved with research activities and hackathon events on campuses in Missouri and Maine. The project will have a web presence at: https://cs.slu.edu/testbed/. Such website will be maintained by the Computer Science Department at Saint Louis University, and will be active at least 5 years beyond the end date of this project. The website will contain links to datasets collected with the testbed, technical reports, scientific publications, and code repositories developed by students and collaborators.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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DOI:
10.1109/tnsm.2023.3287936
发表时间:
2023-09
期刊:
IEEE Transactions on Network and Service Management
影响因子:
5.3
作者:
[Sai Shreyas Bhavanasi;L. Pappone;Flavio Esposito]
通讯作者:
Sai Shreyas Bhavanasi;L. Pappone;Flavio Esposito
DOI:
10.1109/access.2023.3318109
发表时间:
2023
期刊:
IEEE Access
影响因子:
3.9
作者:
[Alessio Sacco;Antonino Angi;G. Marchetto;Flavio Esposito]
通讯作者:
Alessio Sacco;Antonino Angi;G. Marchetto;Flavio Esposito
Howdah: Load Profiling via In-Band Flow Classification and P4
Howdah:通过带内流量分类和 P4 进行负载分析
DOI:
10.23919/cnsm55787.2022.9964510
发表时间:
2022
期刊:
IEEE
影响因子:
--
作者:
[Angi, Antonino, Sacco, Alessio, Esposito, Flavio, Marchetto, Guido, Clemm, Alexander]
通讯作者:
Clemm, Alexander
DOI:
10.1109/nfv-sdn56302.2022.9974607
发表时间:
2022-11
期刊:
2022 IEEE Conference on Network Function Virtualization and Software Defined Networks (NFV-SDN)
影响因子:
--
作者:
[Sai Shreyas Bhavanasi;L. Pappone;Flavio Esposito]
通讯作者:
Sai Shreyas Bhavanasi;L. Pappone;Flavio Esposito
Load Profiling via In-Band Flow Classification and P4 With Howdah
通过带内流量分类和 P4 With Howdah 进行负载分析
DOI:
10.1109/tnsm.2023.3299729
发表时间:
2023
期刊:
IEEE Transactions on Network and Service Management
影响因子:
5.3
作者:
[Angi, Antonino, Sacco, Alessio, Esposito, Flavio, Marchetto, Guido, Clemm, Alexander]
通讯作者:
Clemm, Alexander
共 6 条
Collaborative Research: CPS: TTP Option: Medium: Sharing Farm Intelligence via Edge Computing
-
批准号:2133407
-
项目类别:Standard Grant
-
资助金额:$122.7万
-
财政年份:2022
-
负责人:Flavio Esposito
-
依托单位:
CNS Core: Small: Collaborative Research: HEECMA: A Hybrid Elastic Edge-Cloud Application Management Architecture
-
批准号:1908574
-
项目类别:Standard Grant
-
资助金额:$20.62万
-
财政年份:2019
-
负责人:Flavio Esposito
-
依托单位:
NSF Student Travel Grant for the 2019 ACM CoNEXT Conference
-
批准号:2002096
-
项目类别:Standard Grant
-
资助金额:$1.08万
-
财政年份:2019
-
负责人:Flavio Esposito
-
依托单位:
ICE-T: RI: A Knowledge-Defined Platform for Real-Time Management of Transmissions and Computations at Network Edge
-
批准号:1836906
-
项目类别:Standard Grant
-
资助金额:$10.0万
-
财政年份:2018
-
负责人:Flavio Esposito
-
依托单位:
US Ignite: Collaborative Research: Focus Area 2: Resilient Virtual Path Management for Scalable Data-intensive Computing at Network-Edges
-
批准号:1647084
-
项目类别:Standard Grant
-
资助金额:$20.38万
-
财政年份:2017
-
负责人:Flavio Esposito
-
依托单位:
海外基金