CNS Core: Large: Collaborative Research: Network Design Automation

当我们使用Office365、亚马逊或Facebook时，我们通过网络访问丰富的服务。网络可靠性对于企业、科学和政府来说至关重要，因为停机时间会影响工作效率、经济产出和社交。然而，面对组件故障、手动配置和IT行业严格的经济条件，可靠性很难实现。这个项目将朝着创建一个新的调查领域--网络设计自动化(NDA)迈出第一步。NDA寻求创建基于对网络结构的科学理解来减少设计工作量并提高可靠性的计算机辅助设计(CAD)工具。NDA的灵感来自电子设计自动化(EDA)，EDA是一个价值70亿美元的行业，支撑着1000亿美元的芯片行业，本身就是一门充满活力的智力学科。NDA将使用正式方法验证网络，但也将议程扩大到调试联网系统以及其他专门网络，如农村网络和CDN。具体的新任务包括使用机器学习将应用程序性能与网络问题捆绑在一起，使用自然语言处理了解故障日志，受硬件边界扫描思想启发创建更可控的路由器，使用约束满足进行农村网络设计，为运营商降低门槛的网络脚本编写，以及数据挖掘以在没有规范的情况下发现配置错误。网络面临着独特的挑战，包括异构性、管理复杂性、发展速度(新数据中心和服务推出)以及高可用性需求。除了始终专注于使用算法搜索之外，我们还将利用数据挖掘、机器学习、自然语言处理和硬件测试方法。NDA不仅将重用现有机制，还将利用对现代网络结构的特定领域的见解来发明新的可扩展方法。该奖项反映了NSF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。

When we use Office365, Amazon, or Facebook, we access a world of rich services via a network. Network reliability is paramount for business, science, and government because downtime affects productivity, economic output, and social communication. However, reliability is difficult to achieve in the face of component failure, manual configuration, and the stringent economics of the IT industry. This project will take first steps towards creating a new field of inquiry, Network Design Automation (NDA). NDA seeks to create computer-aided design (CAD) tools that reduce design effort and increase reliability based on a scientific understanding of network structure. NDA is inspired by Electronic Design Automation (EDA), a $7B industry that underpins the $100B chip industry, and is a vibrant intellectual discipline in its own right.NDA will verify networks using formal methods but also broaden the agenda to debugging networked systems and to other specialized networks such as rural networks and CDNs. Specific new tasks include tying application performance to network problems using machine learning, understanding failure logs using Natural Language Processing, creating more controllable routers inspired by hardware boundary scan ideas, rural network design using constraint satisfaction, network scripting to lower the barriers for operators, and data mining to find configuration bugs without a specification. Networks have unique challenges including heterogeneity, management complexity, rate of evolution (new data centers and service rollouts), and high availability needs. Besides a consistent focus on using algorithm search, we will leverage data mining, machine learning, Natural Language Processing, and hardware test approaches. NDA will not merely reuse existing mechanisms but exploit domain-specific insights about the structure of modern networks to invent new and scalable approaches.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.