دانلود رایگان مقاله تاثیر تحلیلگر سیستم نام دامنه بر عملکرد شبکه های توزیع محتوا با استفاده از روش علی

Abstract

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 performance.

6. Concluding remarks

The main contribution of our paper resides in the methodology based on the inference and in the usage of a causal model that allows us to estimate the causal effect of the DNS service on user performance. Using a causal approach and inferring the causal model, which is then represented as a Bayesian graph, we are able to study the causal effect of a DNS service on the TCP throughput. We compare the performance of clients using their ISP local DNS service to the performance of clients using the Google DNS service. The causal model allows to unveil dependencies that would be very difficult, if not impossible, to extract otherwise from the data. We showed that the choice of the DNS service has a strong impact on the location of the servers the clients are redirected to, which in turn impacts not only the distance from clients to servers but also the type of configuration of the servers. Distance and con- figuration are captured by the dependence between the DNS and the RTT and the dependence between the DNS and the server minimum congestion window.