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.

2019冠状病毒病大流行和相关隔离加速了互联网从点对点到以云为中心的模式的根本转变。我们的整个生活都在网上进行，现在主要通过云中的服务进行调解，公共云正在迅速发展，以满足客户和最终用户日益增长的需求。云在现代互联网中的重要性引发了关于现有互联网骨干网络如何支持现在从云提供的应用程序和内容的问题。云提供商可以负担基础设施升级以支持低延迟或高吞吐量应用程序的需求，但他们使基础设施适应应用程序需求的能力仅限于其网络边界。部署和运营传输骨干基础设施的经济性联合收割机与云服务流量的激增相结合，在不断变化的互联网环境中引发了性能瓶颈。该项目提出了一项雄心勃勃的努力，旨在设计测量和分析工具，以改变我们对美国和世界各地云连接性能和可达性的理解。研究人员目前缺乏测量能力，甚至无法大规模识别这些瓶颈，更不用说评估它们对互联网用户的影响了。该项目分为两个任务，它们将联合收割机结合起来，揭示云网络之外的性能瓶颈，在云网络之外，部署和运营的高成本会导致云应用程序的基础设施瓶颈。第一项任务将开发新技术，通过将主动测量与TCP流相结合，来识别云计算中心与数千个可公开访问的速度测试服务器之间的性能瓶颈链接。第二项任务将分析我们通过从云计算中心到整个公共互联网的全面路径测量确定的瓶颈链接，我们将开发新技术，通过地理定位路径退出云网络的位置来支持瓶颈链接的地理位置推断。 该项目的智力价值源于我们将开发和验证的创新方法，以进行现代互联网关键组件的准确，可扩展和可靠的拓扑和性能测量，克服阻碍云测量研究的成本障碍。该项目生成的测量特征和标签将为解决将机器学习技术应用于网络基础设施研究的持续挑战提供理想的基础。该项目还将在科学研究议程之外产生更广泛的影响。该项目产生的工具和数据将对部署到云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设施瓶颈的政策制定者非常有价值。这些数据、工具和分析还可以发现美国宽带性能的不公平性，并为未来的基础设施公共投资提供信息。该奖项反映了NSF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。

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