CNS Core:Small: Optimizing IP Anycast Performance at Scale

CNS 核心：小型：大规模优化 IP 任播性能

• 批准号: 2225448
• 负责人: Xiaowei Yang
• 金额: $ 62.54万
• 依托单位: Duke University
• 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=2225448&HistoricalAwards=false
• 关键词: CNS; Core; Small; Optimizing; IP

Whether it is for online banking, shopping, teleconferencing or gaming, users notice if there is significant delay with an application on the Internet. The networking research community and Service Providers have spent significant time and effort to minimize this interaction latency, and have come a long way in reducing them. One key technique that has been devised is IP anycast. IP anycast allows Internet users to access replicated web content stored at a server close in network distance to them. In theory, a service provider that deploys many replica servers in diverse geographic locations expect to achieve low user latencies. However, in practice, this is often not the case: more replica servers sometimes lead to prolonged latencies. This project aims to provide the research and network operations community with the knowledge and tools to optimize the design of large-scale IP anycast systems such that these systems could achieve low Internet latencies with a minimal number of replica servers. Specifically, this project will develop techniques and tools to enable anycast optimization at scale. It will explore using randomized algorithms to reduce the number of BGP experiments needed for anycast catchment prediction and investigate whether incorporating Internet topology information is effective in further reducing the number of BGP experiments. It plans to study how multi-path routing and non-common BGP policies introduce errors into catchment prediction and whether these practices might improve the accuracy of catchment prediction. It will also examine whether heuristic knowledge such as the number of sites and the site locations and techniques such as divide-and-conquer to further scale anycast can be effective for performance optimization. Finally, the project will look at performance metrics other than latency such as load balancing and resilience.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.

无论是用于在线银行，购物，电信还是游戏，用户都会注意到Internet上的应用程序是否有重大延迟。 网络研究社区和服务提供商花费了大量时间和精力来最大程度地减少这种相互作用的延迟，并且在减少它们方面已经有了很大的帮助。 设计的一种关键技术是IP Anycast。 IP Anycast允许Internet用户访问在网络距离上存储在服务器上的复制Web内容。从理论上讲，在各种地理位置中部署许多复制服务器的服务提供商期望达到低用户潜伏期。但是，实际上，情况通常不是这样：更多的复制服务器有时会导致延长潜伏期。 该项目旨在为研究和网络运营社区提供知识和工具，以优化大规模IP任何广播系统的设计，以便这些系统可以以最少的副本服务器来达到低互联网潜伏期。 具体而言，该项目将开发技术和工具，以使任何广播优化大规模优化。它将使用随机算法进行探索，以减少任何播放集水区预测所需的BGP实验数量，并调查合并互联网拓扑信息是否有效地减少BGP实验的数量。它计划研究多路由路由和非共同的BGP政策如何将错误引入集水预测，以及这些实践是否可以提高集水集预测的准确性。它还将检查启发式知识，例如站点的数量以及站点位置和技术（例如分隔和串扰）以进一步扩展任何广播是否可以有效地进行性能优化。最后，该项目将考虑绩效指标以外的其他等待时间，例如负载平衡和弹性。该奖项反映了NSF的法定任务，并被认为是值得通过基金会的知识分子优点和更广泛的影响评估标准通过评估来支持的。