A study of the impact of DNS resolvers on CDN performance using a causal approach

Resources such as Web pages or videos that are published in the Internet are referred to by their Uniform Resource Locator (URL). If a user accesses a resource via its URL, the host name part of the URL needs to be translated into a routable IP address. This translation is performed by the Domain Name System service (DNS). DNS also plays an important role when Content Distribution Networks (CDNs) are used to host replicas of popular objects on multiple servers that are located in geographically different areas. A CDN makes use of the DNS service to infer client location and direct the client request to the optimal server. While most Internet Service Providers (ISPs) offer a DNS service to their customers, clients may instead use a public DNS service. The choice of the DNS service can impact the performance of clients when retrieving a resource from a given CDN. In this paper we study the impact on download performance for clients using either the DNS service of their ISP or the public DNS service provided by Google DNS. We adopt a causal approach that exposes the structural dependencies of the different parameters impacted by the DNS service used and we show how to model these dependencies with a Bayesian network. The Bayesian network allows us to explain and quantify the performance benefits seen by clients when using the DNS service of their ISP. We also discuss how the further improve client performanc