CC* Integration-Large: Democratizing Networking Research in the Era of AI/ML

自驾车网络的新兴领域为校园网的网络管理员提供了大多数网络管理任务的自动化。这样的自动化确保了网络在各种中断中保持高性能和可靠性，同时只需要网络管理员进行最小的干预。然而，要对自动驾驶网络研究做出重大贡献，就需要开发基于人工智能（AI）和机器学习（ML）的工具，并证明它们在实践中是有效的。不幸的是，与工业界的同行形成鲜明对比的是，大多数学术研究人员既没有获得适当的数据来开发基于学习的工具，也没有适当的仪器测试平台来在现实环境中测试所得到的工具。这个合作项目汇集了来自加州大学圣巴巴拉分校、芝加哥大学和NIKSUN公司的研究人员，研究如何利用校园网络克服自动驾驶网络研究的障碍。首先，它将在两个校园网部署数据包处理管道，在不损害用户隐私的情况下大规模收集适当的网络数据。然后，它将战略性地在校园网络中放置可编程网络设备，以在生产环境中安全地测试新开发的学习模型。最后，它将说明这些新仪器化的校园网络所支持的功能，用于开发、评估和实际测试不同用例的新学习模型。该项目旨在培育一种社区努力，利用校园网络作为自动驾驶网络研究民主化的工具，通过可重复性提高其透明度，并通过建立信任来确保其在实践中的成功。因此，它承诺不仅对整个网络社区，而且对不同的校园网利益相关者（例如，校园it）都具有变革意义。充分利用这些校园网作为数据源和测试平台的双重角色，并将其无缝集成到大学的工程课程中，这为人工智能和机器学习时代的工程专业学生的教学、培训和教育提供了全新的方法。https://democratize-netai.cs.ucsb.edu/.This奖项反映了美国国家科学基金会的法定使命，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。

The emerging area of self-driving networks provides network administrators at campus networks to automate most network-management tasks. Such an automation ensures that the network remains performant and reliable amidst various disruptions, while requiring minimal interventions from the network administrators. However, making significant contributions to self-driving network research requires developing artificial intelligence (AI) and machine learning (ML)-based tools and demonstrating that they work in practice. Unfortunately, in stark contrast to their counterparts in industry, most academic researchers have neither access to the proper data for developing learning-based tools nor have properly instrumented testbeds for road-testing the resulting tools in realistic settings. This collaborative project brings together investigators from the University of California-Santa Barbara, University of Chicago, and NIKSUN Inc., to investigate how to use campus networks to overcome barriers to self-driving network research. First, it will deploy packet-processing pipelines at two campus networks to collect the proper network data at scale without compromising user privacy. It will then strategically place programmable network devices at campus networks to safely road-test newly developed learning models in production settings. Finally, it will illustrate the capabilities enabled by these newly-instrumented campus networks for developing, evaluating, and road-testing new learning models with different use cases.This project is intended to seed a community effort that uses campus networks as vehicles for democratizing self-driving networks research, improving its transparency through reproducibility, and ensuring its success in practice by establishing trust. As such, it promises to be transformative not only for the network community as a whole but also for different campus network stakeholders (e.g., campus IT). Fully leveraging these campus networks' dual role as data source and testbed and seamlessly integrating it into the university's engineering curriculum suggests radically new approaches to teaching, training, and educating engineering students in the era of AI and ML.The project information will be maintained at: https://democratize-netai.cs.ucsb.edu/.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.