A Measurement-based Deployment Proposal for IP Anycast

Despite its growing use in critical infrastructure services, the performance of IP(v4) Anycast and its interaction with IP routing practices is not well understood. As part of this project, we conducted a detailed measurement study of IP Anycast. This study uses a two-pronged approach. First, using a variant of known latency estimation techniques, we measure the performance of current commercially operational IP Anycast deployments from a large number (>20,000) of vantage points. Second, we deploy our own small-scale anycast service that allows us to perform controlled tests under different deployment and failure scenarios. To the best of our knowledge, our study represents the first large-scale evaluation of existing anycast services and the first evaluation of the behavior of IP Anycast under failure.

We find that:

1. IP Anycast, if deployed in an ad-hoc manner, does not offer good latency-based proximity.
2. IP Anycast, if deployed in an ad-hoc manner, does not provide fast failover to clients.
3. IP Anycast typically offers good affinity to all clients with the exception of those that explicitly load balance traffic across multiple providers.
4. IP Anycast, by itself, is not effective in balancing client load across multiple sites.

We thus propose and evaluate practical means by which anycast deployments can achieve good proximity, fast failover and control over the distribution of client load. Overall, our results suggest that an IP Anycast service, if deployed carefully, can offer good proximity, load balance, and failover behavior.

Data Sets used in our IMC paper

1. Complete Data-Set (zipped tarball)
2. Proximity Data-Set (zipped tarball)
3. Affinity Data-Set (zipped tarball)
4. Failover Data-Set (zipped tarball)
5. Load Data-Set (zipped tarball)

Details regarding these data-sets are in the README files included with them.