CNS Core: Medium: Detection and Analysis of Infrastructure Bottlenecks in a Cloud-Centric Internet

CNS 核心：中：以云为中心的互联网中基础设施瓶颈的检测和分析

• 批准号: 2212241
• 负责人: Ka Pui Mok
• 金额: $ 109.2万
• 依托单位: University of California-San Diego
• 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: 2022-10-01 至 2025-09-30
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2212241&HistoricalAwards=false
• 关键词: CNS; Core; Medium; Detection; Analysis

The CoVID-19 pandemic and associated quarantine has accelerated the Internet’s fundamental shift from a peer-to-peer to a cloud-centric model. Our entire lives have moved online, now predominantly mediated by services in the cloud, and public clouds are rapidly evolving to meet increasing requirements and demands from customers and end users. The importance of the clouds in the modern Internet triggers questions regarding how well existing Internet backbone networks support the applications and content now served from the clouds. Cloud providers can afford the infrastructure upgrades to support the needs of low latency or high throughput applications, but their ability to adapt infrastructure to application demands ends at their network border. The economics of deploying and operating transit backbone infrastructure combine with the surge in traffic toward cloud services to induce performance bottlenecks in the changing Internet landscape. This project proposes an ambitious effort to design measurement and analysis tools that can transform our understanding of cloud connectivity performance and reachability in the U.S. and around the world. Researchers currently lack the measurement ability to even identify such bottlenecks at scale, much less assess their impact on Internet users. The project is structured as two tasks that will combine to reveal performance bottlenecks outside the cloud networks where the high cost of deployment and operations leads to infrastructure bottlenecks for cloud applications. The first task will develop novel techniques to identify performance bottleneck links between cloud datacenters and thousands of publicly accessible speed test servers, by synthesizing active measurements with TCP flows. The second task will analyze the bottleneck links we identify with comprehensive path measurements from cloud datacenters to the entire public Internet, and we will develop new techniques to support inference of the geographic locations of bottleneck links by geolocating where paths exit cloud networks. The intellectual merit of this project stems from the innovative methods we will develop and validate to conduct accurate, scalable, and reliable topology and performance measurements of a critical component of the modern Internet, overcoming cost barriers that have prevented measurement studies from the cloud. The measured features and labels the project generates will provide an ideal basis to address the persistent challenge in applying machine learning techniques to network infrastructure research. The project will also have broader impacts outside of the scientific research agenda. The tools and data the project generates will be valuable to enterprises and application developers deploying into the cloud, as well as policy-makers seeking to understand bottlenecks in U.S. Internet infrastructure. The data, tools, and analyses can also lead to the discovery of broadband performance inequities in the U.S. and inform future public investment in infrastructure. Experience with cloud applications and measurements will be incorporated into an undergraduate data science course and undergraduate research mentorships.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.

COVID-19大流行和相关的隔离仪已加速了互联网从点对点到以云为中心的模型的基本转变。我们的一生已经在网上移动，现在主要是由云中的服务介导的，公共云正在迅速发展，以满足客户和最终用户的需求和需求。云在现代互联网中的重要性引发了有关现有的互联网骨干网络如何支持现在从云中提供的应用程序和内容的问题。云提供商可以负担得起基础架构升级，以支持低潜伏期或高吞吐量应用的需求，但是它们适应基础架构的应用程序需求的能力在其网络边框上结束。部署和操作过境骨干基础设施的经济学与云服务的流量激增相结合，以引起不断变化的互联网景观中的性能瓶颈。该项目提出了一个雄心勃勃的努力，以设计测量和分析工具，可以改变我们对美国和世界各地的云连接性能和意识的理解。目前，研究人员缺乏甚至可以大规模识别此类瓶颈的测量能力，更不用说评估其对互联网用户的影响了。该项目的结构是两项任务，将结合起来，以揭示云网络之外的性能瓶颈，在这些任务中，部署和操作的高成本导致了云应用程序的基础架构瓶颈。第一个任务将开发新技术，以通过将主动测量值与TCP流合成活跃的测量值，以识别云数据中心与数千个公共访问速度测试服务器之间的性能瓶颈链接。第二个任务将分析我们与从云数据中心到整个公共互联网的全面路径测量结果所识别的瓶颈链接，我们将开发新技术，以通过地理位置搜索路径退出云网络来支持瓶颈链接的地理位置的推理。我们将开发并验证该项目工厂的智力优点，以对现代互联网的关键组成部分进行准确，可扩展和可靠的拓扑和性能测量，并克服了无法从云中进行测量的成本障碍。该项目生成的测量功能和标签将提供理想的基础，以应对将机器学习技术应用于网络基础架构研究的持续挑战。该项目还将在科学研究议程之外产生更广泛的影响。该项目生成的工具和数据将对部署到云中的企业和应用程序开发人员以及寻求了解美国互联网基础架构中瓶颈的政策制定者很有价值。数据，工具和分析也可能导致在美国发现宽带绩效不平等，并为未来的基础设施投资提供信息。具有云应用和测量的经验将被纳入本科数据科学课程和本科研究心态。该奖项反映了NSF的法定任务，并使用基金会的知识分子优点和更广泛的影响评估标准，认为通过评估被认为是珍贵的支持。

项目成果

Access Denied: Assessing Physical Risks to Internet Access Networks

访问被拒绝：评估互联网访问网络的物理风险

• 发表时间: 2023
• 期刊: USENIX Association
• 作者: Alexander Marder;Zesen Zhang;Ricky Mok;Ramakrishna Padmanabhan;Bradley Huffaker;Matthew Luckie;Alberto Dainotti;kc claffy;Alex C. Snoeren;Aaron Schulman
• 通讯作者: Aaron Schulman