Giotsas, V; (2014) Improving the Accuracy of the Internet Cartography. Doctoral thesis , UCL (University College London);.

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Abstract

As the global Internet expands to satisfy the demands of the ever-increasing connected population, profound changes are occurring in its interconnection structure. The pervasive growth of IXPs and CDNs, two initially independent but synergistic infrastructure sectors, have contributed to the gradual flattening of the Internet’s inter-domain hierarchy with primary routing paths shifting from backbone networks to peripheral peering links. At the same time the IPv6 deployment has taken off due to the depletion of unallocated IPv4 addresses. These fundamental changes in Internet dynamics has obvious implications for network engineering and operations, which can be benefited by accurate topology maps to understand the properties of this critical infrastructure. This thesis presents a set of new measurement techniques and inference algorithms to construct a new type of semantically rich Internet map, and improve the state of the art in Internet cartography. The author first develops a methodology to extract large-scale validation data from the Communities BGP attribute, which encodes rich routing meta-data on BGP messages. Based on this better-informed dataset the author proceeds to analyse popular assumptions about inter-domain routing policies and devise a more accurate model to describe inter-AS business relationships. Accordingly, the thesis proposes a new relationship inference algorithm to accurately capture both simple and complex AS relationships across two dimensions: prefix type, and geographic location. Validation against three sources of ground-truth data reveals that the proposed algorithm achieves a near-perfect accuracy. However, any inference approach is constrained by the inability of the existing topology data sources to provide a complete view of the inter-domain topology. To limit the topology incompleteness problem the author augments traditional BGP data with routing policy data obtained directly from IXPs to discover massive peering meshes which have thus far been largely invisible.

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