The type of business relationships between the Internet autonomous systems (AS) determines the BGP inter-domain routing. Previous works on inferring AS relationships relied on the connectivity information between ASes. In this paper we infer AS relationships by analysing the routing polices of ASes encoded in the BGP attributes Communities and the Locpref. We accumulate BGP data from RouteViews, RIPE RIS and the public Route Servers in August 2010 and February 2011. Based on the routing policies extracted from data of the two BGP attributes, we obtain AS relationships for 39 links in our data, which include all links among the Tier-1 ASes and most links between Tier-1 and Tier-2 ASes. We also reveal a number of special AS relationships, namely the hybrid relationship, the partial-transit relationship, the indirect peering relationship and the backup links. These special relationships are relevant to a better understanding of the Internet routing. Our work provides a profound methodological progress for inferring the AS relationships.
These works are common in that they mainly relied on a single data source, i.e. the AS connectivity data. Although sophisticated heuristics have been used, the connectivity data itself is inherently limited in providing useful information for inferring AS relationships. Some heuristics can even introduce errors. Inference results produced by different existing algorithms are often inconsistent and sometimes conflict to each other. Two recent works @cite_9 @cite_2 showed that BGP simulations based on these data lead to poor results. In addition, existing algorithms are not capable of discovering any unconventional AS relationships.
@cite_9 @cite_2
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Research on performance, robustness, and evolution of the global Internet is fundamentally handicapped without accurate and thorough knowledge of the nature and structure of the contractual relationships between Autonomous Systems (ASs). In this work we introduce novel heuristics for inferring AS relationships. Our heuristics improve upon previous works in several technical aspects, which we outline in detail and demonstrate with several examples. Seeking to increase the value and reliability of our inference results, we then focus on validation of inferred AS relationships. We perform a survey with ASs' network administrators to collect information on the actual connectivity and policies of the surveyed ASs. Based on the survey results, we find that our new AS relationship inference techniques achieve high levels of accuracy: we correctly infer 96.5 customer to provider (c2p), 82.8 peer to peer (p2p), and 90.3 sibling to sibling (s2s) relationships. We then cross-compare the reported AS connectivity with the AS connectivity data contained in BGP tables. We find that BGP tables miss up to 86.2 of the true adjacencies of the surveyed ASs. The majority of the missing links are of the p2p type, which highlights the limitations of present measuring techniques to capture links of this type. Finally, to make our results easily accessible and practically useful for the community, we open an AS relationship repository where we archive, on a weekly basis, and make publicly available the complete Internet AS-level topology annotated with AS relationship information for every pair of AS neighbors. The commercial contractual relationship among autonomous systems (ASs) is important for understanding the reachability and traffic flow in the Internet. Several heuristic algorithms have been proposed for inferring AS relationships by extracting information from border gateway protocol (BGP) routing tables. However, there is no evaluation of the AS relationships inferred from existing algorithms. In this paper, we present the methodology for obtaining partial AS relationships, and use them to evaluate these algorithms. Our result shows that although existing algorithms achieve good overall accuracy, the accuracy on the peer-to-peer relationships is quite poor. Furthermore, we propose an algorithm for inferring AS relationships by taking advantage of this partial information. We show that our proposed algorithm outperforms existing algorithms for inferring peer-to-peer relationships by 1.8 spl sim 3.8 times in accuracy. In addition, the overall accuracy of our algorithm is higher than that of existing algorithms.