CNS Core: Large: Collaborative Research: Network Design Automation

CNS 核心：大型：协作研究：网络设计自动化

• 批准号: 1901523
• 负责人: Ramesh Govindan
• 金额: $ 100万
• 依托单位: University of Southern California
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2019
• 资助国家: 美国
• 起止时间: 2019-07-01 至 2025-06-30
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1901523&HistoricalAwards=false
• 关键词: CNS; Core; Large; Collaborative; Research

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.

当我们使用Google 365、亚马逊或Facebook时，我们通过网络访问了一个丰富的服务世界。网络可靠性对商业、科学和政府至关重要，因为停机会影响生产力、经济产出和社会沟通。然而，面对组件故障、手动配置和IT行业严格的经济性，可靠性很难实现。该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旨在创建计算机辅助设计（CAD）工具，以减少设计工作并基于对网络结构的科学理解提高可靠性。NDA的灵感来自于电子设计自动化（EDA），EDA是一个价值70亿美元的行业，支撑着价值1000亿美元的芯片行业，并且本身就是一个充满活力的知识学科。NDA将使用正式的方法来验证网络，但也将扩展议程到调试联网系统和其他专业网络，如农村网络和CDN。 具体的新任务包括使用机器学习将应用性能与网络问题联系起来，使用自然语言处理理解故障日志，受硬件边界扫描思想的启发创建更可控的路由器，使用约束满足的农村网络设计，降低运营商障碍的网络脚本，以及在没有规范的情况下发现配置错误的数据挖掘。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将不仅重用现有的机制，而且利用对现代网络结构的特定领域的见解来发明新的和可扩展的方法。该奖项反映了NSF的法定使命，并被认为值得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来支持。

项目成果

Semi-automated protocol disambiguation and code generation

• DOI: 10.1145/3452296.3472910
• 发表时间: 2020-10
• 期刊: Proceedings of the 2021 ACM SIGCOMM 2021 Conference
• 作者: Jane Yen;Tam'as L'evai;Qinyuan Ye;Xiang Ren;R. Govindan;B. Raghavan

A throughput-centric view of the performance of datacenter topologies

• DOI: 10.1145/3452296.3472913
• 发表时间: 2021-08
• 期刊: Proceedings of the 2021 ACM SIGCOMM 2021 Conference
• 作者: Pooria Namyar;Sucha Supittayapornpong;Mingyang Zhang;Minlan Yu;R. Govindan

Rapid Top-Down Synthesis of Large-Scale IoT Networks

大规模物联网网络的快速自上而下综合

• DOI: 10.1109/icccn49398.2020.9209680
• 发表时间: 2020
• 期刊: Proceedings of the 29th International Conference on Computer Communications and Networks (ICCCN 2020
• 作者: Ghosh, Pradipta;Bunton, Jonathan;Pylorof, Dimitrios;Vieira, Marcos;Chan, Kevin;Govindan, Ramesh;Sukhatme, Gaurav;Tabuada, Paulo;Verma, Gunjan
• 通讯作者: Verma, Gunjan