AnyOpt: Predicting and optimizing IP Anycast performance

Xiao Zhang, Tanmoy Sen, Zheyuan Zhang, Tim April, Balakrishnan Chandrasekaran, David Choffnes, Bruce M. Maggs, Haiying Shen, Ramesh K. Sitaraman, Xiaowei Yang

Research output: Chapter in Book / Report / Conference proceedingConference contributionAcademicpeer-review

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Abstract

The key to optimizing the performance of an anycast-based system (e.g., the root DNS or a CDN) is choosing the right set of sites to announce the anycast prefix. One challenge here is predicting catchments. A naïve approach is to advertise the prefix from all subsets of available sites and choose the best-performing subset, but this does not scale well. We demonstrate that by conducting pairwise experiments between sites peering with tier-1 networks, we can predict the catchments that would result if we announce to any subset of the sites. We prove that our method is effective in a simplified model of BGP, consistent with common BGP routing policies, and evaluate it in a real-world testbed. We then present AnyOpt, a system that predicts anycast catchments. Using AnyOpt, a network operator can find a subset of anycast sites that minimizes client latency without using the naïve approach. In an experiment using 15 sites, each peering with one of six transit providers, AnyOpt predicted site catchments of 15,300 clients with 94.7% accuracy and client RTTs with a mean error of 4.6%. AnyOpt identified a subset of 12 sites, announcing to which lowers the mean RTT to clients by 33ms compared to a greedy approach that enables the same number of sites with the lowest average unicast latency.

Original languageEnglish
Title of host publicationSIGCOMM 2021
Subtitle of host publicationProceedings of the ACM SIGCOMM 2021 Conference
PublisherAssociation for Computing Machinery, Inc
Pages447-462
Number of pages16
ISBN (Electronic)9781450383837
DOIs
Publication statusPublished - Aug 2021
Event2021 Annual Conference of the ACM Special Interest Group on Data Communication on the Applications, SIGCOMM 2021 - Virtual, Online, United States
Duration: 23 Aug 202127 Aug 2021

Conference

Conference2021 Annual Conference of the ACM Special Interest Group on Data Communication on the Applications, SIGCOMM 2021
Country/TerritoryUnited States
CityVirtual, Online
Period23/08/2127/08/21

Bibliographical note

Funding Information:
We thank the anonymous reviewers and our shepherd Jennifer Rex-ford for their helpful comments, and Haoyu Wang and Shen Zhu for helping with an early draft of this paper. We sincerely thank the network engineering team at Akamai Technologies, especially Aaron Block and Aaron Atac, whose help made this work possible. We thank Kamesh Munagala for help in proving that even approximating the minimum cost of SPLPO is NP-hard. This work was supported in part by the National Science Foundation under awards 1910867, 1763617, 1763742, 1822965, and 1827674, and in part by subcontracts from Akamai Technologies in support of DARPA prime contract HR0011-17-C-0030. Additional support was provided by Microsoft Research Faculty Fellowship 8300751 and AWS Machine Learning Research awards.

Publisher Copyright:
© 2021 ACM.

Funding

We thank the anonymous reviewers and our shepherd Jennifer Rex-ford for their helpful comments, and Haoyu Wang and Shen Zhu for helping with an early draft of this paper. We sincerely thank the network engineering team at Akamai Technologies, especially Aaron Block and Aaron Atac, whose help made this work possible. We thank Kamesh Munagala for help in proving that even approximating the minimum cost of SPLPO is NP-hard. This work was supported in part by the National Science Foundation under awards 1910867, 1763617, 1763742, 1822965, and 1827674, and in part by subcontracts from Akamai Technologies in support of DARPA prime contract HR0011-17-C-0030. Additional support was provided by Microsoft Research Faculty Fellowship 8300751 and AWS Machine Learning Research awards.

Keywords

  • Anycast
  • BGP
  • performance optimization
  • routing

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