Taming Anycast in a Wild Internet

McQuistin, S. , Uppu, S. P. and Flores, M. (2019) Taming Anycast in a Wild Internet. In: ACM Internet Measurement Conference 2019, Amsterdam, The Netherlands, 21-23 Oct 2019, pp. 165-178. ISBN 9781450369480 (doi: 10.1145/3355369.3355573)

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Abstract

Anycast is a popular tool for deploying global, widely available systems, including DNS infrastructure and content delivery networks (CDNs). The optimization of these networks often focuses on the deployment and management of anycast sites. However, such approaches fail to consider one of the primary configurations of a large anycast network: the set of networks that receive anycast announcements at each site (i.e., an announcement configuration). Altering these configurations, even without the deployment of additional sites, can have profound impacts on both anycast site selection and round-trip times. In this study, we explore the operation and optimization of any-cast networks through the lens of deployments that have a large number of upstream service providers. We demonstrate that these many-provider anycast networks exhibit fundamentally different properties when interacting with the Internet, having a greater number of single AS hop paths and reduced dependency on each provider, compared with few-provider networks. We further examine the impact of announcement configuration changes, demonstrating that in nearly 30% of vantage point groups, round-trip time performance can be improved by more than 25%, solely by manipulating which providers receive anycast announcements. Finally, we propose DailyCatch, an empirical measurement methodology for testing and validating announcement configuration changes, and demonstrate its ability to influence user-experienced performance on a global anycast CDN.

Item Type:Conference Proceedings
Status:Published
Refereed:Yes
Glasgow Author(s) Enlighten ID:McQuistin, Dr Stephen
Authors: McQuistin, S., Uppu, S. P., and Flores, M.
College/School:College of Science and Engineering > School of Computing Science
ISBN:9781450369480
Copyright Holders:Copyright © 2019 Association for Computing Machinery
Publisher Policy:Reproduced in accordance with the copyright policy of the publisher
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