Inferring Complex AS Relationships

Abstract

The traditional approach of modeling relationships between ASes abstracts relationship types into three broad categories: transit, peering, and sibling. More complicated configurations exist, and understanding them may advance our knowledge of Internet economics and improve models of routing. We use BGP, traceroute, and geolocation data to extend CAIDA's AS relationship inference algorithm to infer two types of complex relationships: hybrid relationships, where two ASes have different relationships at different interconnection points, and partial transit relationships, which restrict the scope of a customer relationship to the provider's peers and customers. Using this new algorithm, we find 4.5% of the 90,272 provider-customer relationships observed in March 2014 were complex, including 1,071 hybrid relationships and 2,955 partial-transit relationships. Because most peering relationships are invisible, we believe these numbers are lower bounds. We used feedback from operators, and relationships encoded in BGP communities and RPSL, to validate 20% and 6.9% of our partial transit and hybrid inferences, respectively, and found our inferences have 92.9% and 97.0% positive predictive values. Hybrid relationships are not only established between large transit providers; in 57% of the inferred hybrid transit/peering relationships the customer had a customer cone of fewer than 5 ASes.

Measuring and Mitigating AS-level Adversaries Against Tor

“…To address the challenges of reliably measuring reversepaths or use control-plane mapping tools, we employ an efficient path prediction approach which leverages up-to-date maps of the AS-level Internet topology [23], and algorithmic simulations that take into account a common model of routing policies [22].…”

“…However, in practice AS relationships may violate this simple taxonomy e.g., ASes that agree to provide transit for a subset of prefixes (partial transit) or ASes that have different economic arrangements in different geographic regions (hybrid relationships) [23]. It can also be the case that two ASes are controlled by the same organization e.g., because of corporate mergers such as Level 3 (AS3356) and Global Crossing (AS3549) or organizations that leverage different AS numbers in different regions such as Verizon (AS701, 702, 703).…”

An Analysis of the Largest National Ecosystem of Public Internet eXchange Points: The Case of Brazil

“…The numerical results of PA are publicly available in our repository 14 . Taking as example the peering affinity between members of categories 1.2 (Access Providers) and 2.1 (Content Providers), the amount of connections between all peered ASes of categories 1.2 and 2.1 totals 98, divided by the number of vertices of both categories (236) 15 , returns 0.42 as the cross-AS-type peering affinity metric. Figure 5 presents the result of the nation-wide analysis regarding peering affinity with the color scale being a function of the ratio between the sum of connections (peering between ASes) and the number of vertices of both crossed categories.…”

“…According to Giotsas et al [15], the relationships between ASes are traditionally classified as: (i) transit (provider-tocustomer), (ii) peering (peer-to-peer) and (iii) sibling (similar domains), however there are advanced settings that imply in hybrid or complex relations. To better understand these complex relationships, the CAIDA [5] algorithm was improved to be capable of automatically parses BGP tables, outputs from traceroute and geolocation data.…”

“…traceroute), and (iii) BGP tables available in route servers [1,5,15,16,25,26,34]. IXPs have become attractive research targets because they represent a microcosm of Internet diversity [7], serving a variety of members such as ISPs of different nature, content providers, as well as public and private organizations.…”