The discovery of Autonomous Systems (ASes) interconnections and the inference of their commercial Type-of-Relationships (ToR) has been extensively studied during the last few years. The main motivation is to accurately calculate AS-level paths and to provide better topological view of the Internet. An inherent problem in current algorithms is their extensive use of heuristics. Such heuristics incur unbounded errors which are spread over all inferred relationships. We propose a near-deterministic algorithm for solving the ToR inference problem. Our algorithm uses as input the Internet core, which is a dense sub-graph of top-level ASes. We test several methods for creating such a core and demonstrate the robustness of the algorithm to the core's size and density, the inference period, and errors in the core. We evaluate our algorithm using AS-level paths collected from RouteViews BGP paths and DIMES traceroute measurements. Our proposed algorithm deterministically infers over 95 of the approximately 58,000 AS topology links. The inference becomes stable when using a week worth of data and as little as 20 ASes in the core. The algorithm infers 2-3 times more peer-to-peer relationships in edges discovered only by DIMES than in RouteViews edges, validating the DIMES promise to discover periphery AS edges.
Dimitropoulos al @cite_1 addressed a problem in current ToR algorithms. They showed that although ToR algorithms produce a directed Internet graph with a very small number of invalid paths, the resulting AS relationships are far from reality. This led them to the conclusion that simply trying to maximize the number of valid paths (namely improving the result of the ToR algorithms) does not produce realistic results. Later in @cite_17 they showed that ToR has no means to deterministically select the most realistic solution when facing multiple possible solutions. In order to solve this problem, the authors suggested a new objective function by adding a notion of "AS importance", which is the AS degree "gradient" in the original undirected Internet graph. The modified ToR algorithm directs the edges from low importance AS to a higher one. The authors showed that although they have high success rate in p2c inference (96.5 in s2s inference (90.3 (82.8 and mention that for some of them, the BGP tables, which are the source for AS-level routing paths for most works in this research field, miss up to 86.2 ASes, most of which are of p2p type.
@cite_1 @cite_17
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Recent techniques for inferring business relationships between ASs [1,2] have yielded maps that have extremely few invalid BGP paths in the terminology of Gao[3]. However, some relationships inferred by these newer algorithms are incorrect, leading to the deduction of unrealistic AS hierarchies. We investigate this problem and discover what causes it. Having obtained such insight, we generalize the problem of AS relationship inference as a multiobjective optimization problem with node-degree-based corrections to the original objective function of minimizing the number of invalid paths. We solve the generalized version of the problem using the semidefinite programming relaxation of the MAX2SAT problem. Keeping the number of invalid paths small, we obtain a more veracious solution than that yielded by recent heuristics. 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.