Although the Internet AS-level topology has been extensively studied over the past few years, little is known about the details of the AS taxonomy. An AS "node" can represent a wide variety of organizations, e.g., large ISP, or small private business, university, with vastly different network characteristics, external connectivity patterns, network growth tendencies, and other properties that we can hardly neglect while working on veracious Internet representations in simulation environments. In this paper, we introduce a radically new approach based on machine learning techniques to map all the ASes in the Internet into a natural AS taxonomy. We successfully classify 95.3 of ASes with expected accuracy of 78.1 . We release to the community the AS-level topology dataset augmented with: 1) the AS taxonomy information and 2) the set of AS attributes we used to classify ASes. We believe that this dataset will serve as an invaluable addition to further understanding of the structure and evolution of the Internet.
The delivery of IP traffic through the Internet depends on the complex interactions between thousands of autonomous systems (AS) that exchange routing information using the border gateway protocol (BGP). This paper investigates the topological structure of the Internet in terms of customer-provider and peer-peer relationships between autonomous systems, as manifested in BGP routing policies. We describe a technique for inferring AS relationships by exploiting partial views of the AS graph available from different vantage points. Next we apply the technique to a collection of ten BGP routing tables to infer the relationships between neighboring autonomous systems. Based on these results, we analyze the hierarchical structure of the Internet and propose a five-level classification of AS. Our characterization differs from previous studies by focusing on the commercial relationships between autonomous systems rather than simply the connectivity between the nodes. The study of the Internet topology has recently received much attention from the research community. In particular, the observation that the network graph has interesting properties, such as power laws, that might be explored in a myriad of ways. Most of the work in characterizing the Internet graph is based on the physical network graph, i.e., the connectivity graph. In this paper we investigate how logical relationships between nodes of the AS graph can be used to gain insight to its structure. We characterize the logical graph using various metrics and identify the presence of power laws in the number of customers that a provider has. Using these logical relationships we define a structural model of the AS graph. The model highlights the hierarchical nature of logical relationships and the preferential connection to larger providers. We also investigate the consistency of this model over time and observe interesting properties of the hierarchical structure. The Internet routing fabric is partitioned into several domains. Each domain represents a region of the fabric administered by a single commercial entity. Over the past two years, the routing fabric has experienced significant growth. From more than a year's worth of inter-domain routing traces, we analyze the Internet inter-domain topology, its route stability behavior, and the effect of growth on these characteristics. Our analysis reveals several interesting results. Despite growth, the degree distribution and the diameter of the inter-domain topology have remained relatively unchanged. Furthermore, there exists a four-level hierarchy of Internet domains classified by degree. However, connectivity between domains is significantly non-hierarchical. Despite increased connectivity at higher levels in the topology, the distribution of paths to prefixes from the backbone remained relatively unchanged. There is evidence that both route availability and the mean reachability duration have degraded with Internet growth.