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

自动驾驶网络的新兴领域为园区网络的网络管理员提供了自动化大多数网络管理任务的能力。这样的自动化确保网络在各种中断中保持性能和可靠性，同时只需要最少的网络管理员干预。然而，要对自动驾驶网络研究做出重大贡献，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有效。不幸的是，与工业同行形成鲜明对比的是，大多数学术研究人员既无法获得开发基于学习的工具所需的适当数据，也没有适当的仪器试验台在现实环境中对所产生的工具进行道路测试。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 Inc.的研究人员，以调查如何利用校园网络来克服自动驾驶网络研究的障碍。首先，它将在两个园区网络部署数据包处理管道，以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，以便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校园网在开发、评估和道路测试不同用例的新学习模式方面的能力。该项目旨在推动社区努力，利用校园网作为民主化自动驾驶网络研究的工具，通过可重复性提高其透明度，并通过建立信任确保其在实践中取得成功。因此，它不仅对整个网络社区，而且对不同的校园网络利益相关者(例如，校园IT)都具有变革性。充分利用这些校园网作为数据源和试验台的双重作用，并将其无缝地整合到大学的工程课程中，这表明在人工智能和ML时代，教学、培训和教育工程专业学生的全新方法。项目信息将保留在：https://democratize-netai.cs.ucsb.edu/.This奖反映了国家科学基金会的法定使命，并通过使用基金会的智力优势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.