Proactive and Passive Performance Optimization of IP Anycast

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IP Anycast, as a vital routing technique, can distribute user requests to different servers with the same IP worldwide. It can improve large-scale distributed systems performance and load balance. Nonetheless, all the sites in the anycast-based system have identical IP addresses, which makes it challenging to control the system’s catchment (which site the user should go to) and results in anycast performance inefficiency.

In this thesis, we introduce two approaches to optimize the performance of IP anycast, proactively and passively. The first approach-AnyOpt, managed to build a prediction model to predict the catchment site of the user with controlled experiments and measurements with the sites. Using AnyOpt, a network operator can find a subset of anycast sites that minimizes client latency. 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 33 ms compared to a greedy approach that enables the same number of sites with the lowest average unicast latency.

The second approach-regional anycast, is an approach that we found to have already been implemented by two large CDNs (Edgio and Imperva). In regional anycast, a CDN divides its content-hosting sites into different geographic regions, announces a distinct IP anycast prefix from each region, and uses DNS and IP-geolocation to direct a client to a CDN site in the same geographic area. We aim to understand how a regional anycast CDN partitions its sites and maps its customers’ clients, and how a regional anycast CDN performs compared to its global anycast counterpart. We study the deployment strategies and the performance of two CDNs (Edgio and Imperva) that currently deploy regional IP anycast. We find that both Edgio and Imperva partition their sites and clients following continent or country borders. In addition, we compare the client latency distribution in Imperva’s regional anycast CDN with that in its similar-scale DNS global anycast network, after discounting the relevant deployment differences between the two networks. We find that regional anycast can effectively mitigate the pathology in global IP anycast where BGP routes a client’s traffic to a distant CDN site (e.g., a site in a different continent). However, DNS mapping inefficiencies, where DNS returns a sub-optimal regional IP anycast address that does not cover a client’s low-latency CDN sites, can harm regional anycast’s performance. Finally, using the Tangled testbed, we show what performance benefit regional IP anycast can achieve if we discount DNS mapping sub-optimality.

We also include a measurement work about the ever-increasing anycast flipping. We observe an increase in flipping over the past several years, reaching 4.4% of RIPE Atlas vantage points in 2023. We present evidence that the prevalence of anycast flipping is increasing, and for a small but not negligible portion of clients, the impact on web performance is significant.





Zhang, Xiao (2023). Proactive and Passive Performance Optimization of IP Anycast. Dissertation, Duke University. Retrieved from


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