CC* Integration-Small: A Software-Defined Edge Infrastructure Testbed for Full-stack Data-Driven Wireless Network Applications

CC* Integration-Small：用于全栈数据驱动无线网络应用的软件定义边缘基础设施测试台

• 批准号: 2201536
• 负责人: Flavio Esposito
• 金额: $ 50万
• 依托单位: Saint Louis University
• 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
• 起止时间: 2022-07-01 至 2024-06-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2201536&HistoricalAwards=false
• 关键词: CC; Integration; Small; Software; Defined

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.

跨学科研究的进步通常需要设备通过高带宽链路收集、处理和传输大型科学数据集。 该项目的总体目标是与东北大学合作，在圣刘易斯大学建立一个无线虚拟网络测试平台，以评估将机器学习和人工智能与可编程无线电和可编程网络交换机相结合的网络管理解决方案。为了评估计算机网络的拟议创新，网络基础设施将被用于原型网络协议和系统，以支持校园内的一些跨学科的倡议，特别是，这个项目的贡献将围绕学习技术与网络机制，如媒体访问控制，路由和传输服务的整合发展。首先，该团队将探索有效的传输和路由协议的设计和实现，这些协议使用强化学习的最新进展在不同范围内集成网络堆栈。其次，将提出新的网络架构，将边缘网络机制与联邦和分裂学习技术相结合。第三，跨层分布式学习协议将被设计用于创建自适应无线网络。这些解决方案将在校园和其他网络测试平台上进行测试。通过结合数据科学和网络虚拟化协议和架构领域的协同作用，这项工作将为进一步研究（无线）边缘计算应用的自适应资源管理奠定基础，从而提高我们社会的生活质量。该项目的结果将对其他对实时预测感兴趣的领域有价值，如机器人技术，医学，人类学和金融。该项目的研究也将由于计划的行业和国际合作而产生影响。来自代表性不足群体的学生将参与密苏里州和缅因州校园的研究活动和黑客活动。该项目将在https://cs.slu.edu/testbed/网站上公布。该网站将由圣刘易斯大学计算机科学系维护，并将在本项目结束后至少5年内保持活跃。该网站将包含与测试平台收集的数据集、技术报告、科学出版物以及学生和合作者开发的代码库的链接。该奖项反映了NSF的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。

项目成果

Dealing With Changes: Resilient Routing via Graph Neural Networks and Multi-Agent Deep Reinforcement Learning

• 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

P4FL: An Architecture for Federating Learning With In-Network Processing

• 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

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
• 作者: Angi, Antonino;Sacco, Alessio;Esposito, Flavio;Marchetto, Guido;Clemm, Alexander
• 通讯作者: Clemm, Alexander

Routing with Graph Convolutional Networks and Multi-Agent Deep Reinforcement Learning

• 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)
• 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