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

新冠肺炎的流行和相关的隔离加速了互联网从点对点模式向以云为中心的模式的根本性转变。我们的整个生活都转移到了网络上，现在主要由云中的服务来协调，公共云正在快速发展，以满足客户和最终用户日益增长的需求和要求。云在现代互联网中的重要性引发了人们的疑问，即现有的互联网主干网络对现在通过云提供的应用和内容的支持程度如何。云提供商可以承担基础设施升级，以支持低延迟或高吞吐量应用的需求，但他们根据应用需求调整基础设施的能力仅限于其网络边界。部署和运营中转主干基础设施的经济性，加上流向云服务的流量激增，导致不断变化的互联网环境中出现性能瓶颈。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的云连接性能和可达性的理解。研究人员目前甚至缺乏识别规模上的瓶颈的测量能力，更不用说评估它们对互联网用户的影响了。该项目被组织为两个任务，这两个任务将结合起来揭示云网络之外的性能瓶颈，其中高昂的部署和运营成本导致云应用的基础设施瓶颈。第一个任务将开发新的技术，通过将活动测量与TCP流相结合，识别云数据中心和数千台可公开访问的速度测试服务器之间的性能瓶颈链路。第二项任务将通过从云数据中心到整个公共互联网的全面路径测量来分析我们识别的瓶颈链路，我们将开发新的技术，通过对路径离开云网络的位置进行地理定位来支持瓶颈链路的地理位置推断。该项目的智慧价值来自于我们将开发和验证的创新方法，以对现代互联网的关键组件进行准确、可扩展和可靠的拓扑和性能测量，从而克服阻碍从云进行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方面的长期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设施瓶颈的政策制定者具有价值。这些数据、工具和分析还可能导致发现美国的宽带性能不平等，并为未来的公共基础设施投资提供信息。云应用和测量的经验将被纳入本科生数据科学课程和本科生研究指导。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。

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.