Formally, the Internet inter-domain routing system is a collection of networks, their policies, peering relationships and organizational affiliations, and the addresses they advertize. It also includes components like Internet exchange points. By its very definition, each and every aspect of this system is impacted by BGP, the de-facto standard inter-domain routing protocol. The element of this inter-domain routing system that has attracted the single-most attention within the research community has been the "inter-domain topology". Unfortunately, almost from the get go, the vast majority of studies of this topology, from definition, to measurement, to modeling and analysis, have ignored the central role of BGP in this problem. The legacy is a set of specious findings, unsubstanciated claims, and ill-conceived ideas about the Internet as a whole. By presenting a BGP-focused state-of-the-art treatement of the aspects that are critical for a rigorous study of this inter-domain topology, we de-mythify in this paper many "controversial" observations reported in the existing literature. At the same time, we illustrate the benefits and richness of new scientific approaches to measuring, modeling, and analyzing the interdomain topology that are faithful to the BGP-specific nature of this problem domain.

This paper presents a methodology for identifying the autonomous system (or systems) responsible when a routing change is observed and propagated by BGP. The origin of such a routing instability is deduced by examining and correlating BGP updates for many prefixes gathered at many observation points. Although interpreting BGP updates can be perplexing, we find that we can pinpoint the origin to either a single AS or a session between two ASes in most cases. We verify our methodology in two phases. First, we perform simulations on an AS topology derived from actual BGP updates using routing policies that are compatible with inferred peering/customer/provider relationships. In these simulations, in which network and router behavior are "ideal", we inject inter-AS link failures and demonstrate that our methodology can effectively identify most origins of instability. We then develop several heuristics to cope with the limitations of the actual BGP update propagation process and monitoring infrastructure, and apply our methodology and evaluation techniques to actual BGP updates gathered at hundreds of observation points. This approach of relying on data from BGP simulations as well as from measurements enables us to evaluate the inference quality achieved by our approach under ideal situations and how it is correlated with the actual quality and the number of observation points.

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An understanding of the topological structure of the Internet is needed for quite a number of networking tasks, e. g., making decisions about peering relationships, choice of upstream providers, inter-domain traffic engineering. One essential component of these tasks is the ability to predict routes in the Internet. However, the Internet is composed of a large number of independent autonomous systems (ASes) resulting in complex interactions, and until now no model of the Internet has succeeded in producing predictions of acceptable accuracy.We demonstrate that there are two limitations of prior models: (i) they have all assumed that an Autonomous System (AS) is an atomic structure - it is not, and (ii) models have tended to oversimplify the relationships between ASes. Our approach uses multiple quasi-routers to capture route diversity within the ASes, and is deliberately agnostic regarding the types of relationships between ASes. The resulting model ensures that its routing is consistent with the observed routes. Exploiting a large number of observation points, we show that our model provides accurate predictions for unobserved routes, a first step towards developing structural mod-els of the Internet that enable real applications.

This paper presents a methodology for identifying the autonomous system (or systems) responsible when a routing change is observed and propagated by BGP. The origin of such a routing instability is deduced by examining and correlating BGP updates for many prefixes gathered at many observation points. Although interpreting BGP updates can be perplexing, we find that we can pinpoint the origin to either a single AS or a session between two ASes in most cases. We verify our methodology in two phases. First, we perform simulations on an AS topology derived from actual BGP updates using routing policies that are compatible with inferred peering/customer/provider relationships. In these simulations, in which network and router behavior are "ideal", we inject inter-AS link failures and demonstrate that our methodology can effectively identify most origins of instability. We then develop several heuristics to cope with the limitations of the actual BGP update propagation process and monitoring infrastructure, and apply our methodology and evaluation techniques to actual BGP updates gathered at hundreds of observation points. This approach of relying on data from BGP simulations as well as from measurements enables us to evaluate the inference quality achieved by our approach under ideal situations and how it is correlated with the actual quality and the number of observation points.