Abstract-Recent studies concerning the Internet connectivity at the AS level have attracted considerable attention. These studies have exclusively relied on the BGP data from Oregon route-views [1] to derive some unexpected and intriguing results. The Oregon route-views data sets reflect AS peering relationships, as reported by BGP, seen from a handful of vantage points in the global Internet. The possibility that these data sets from Oregon route-views may provide only a very sketchy picture of the complete inter-AS connections that exist in the actual Internet has received surprisingly little scrutiny. In this paper, we will use the term "AS peering relationship" to mean that there is "at least one direct router-level connection" between two existing ASs, and that these two ASs agree to exchange traffic by enabling BGP between them. By augmenting the Oregon route-views data sets with BGP summary information from a large number of Internet Looking Glass sites and with routing policy information from Internet Routing Registry (IRR) databases, we find that (1) a significant number of existing AS connections remain hidden from most BGP routing tables, (2) the AS connections to tier-1 ASs are in general more easily observed than those to non tier-1 ASs, and (3) there are at least about 25-50% more AS connections in the Internet than commonly-used BGP-derived AS maps reveal (but only about 2% more ASs). These findings point out the need for an increased awareness of and a more critical attitude toward the applicability and completeness of given data sets at hand when establishing the generality of any particular observations about the Internet.

Abstract-Internet connectivity at the AS level, defined in terms of pairwise logical peering relationships, is constantly evolving. This evolution is largely a response to economic, political, and technological changes that impact the way ASs conduct their business. We present a new framework for modeling this evolutionary process by identifying a set of criteria that ASs consider either in establishing a new peering relationship or in reassessing an existing relationship. The proposed framework is intended to capture key elements in the decision processes underlying the formation of these relationships. We present two decision processes that are executed by an AS, depending on its role in a given peering decision, as a customer or a peer of another AS. When acting as a peer, a key feature of the AS's corresponding decision model is its reliance on realistic inter-AS traffic demands. To reflect the enormous heterogeneity among customer or peer ASs, our decision models are flexible enough to accommodate a wide range of AS-specific objectives. We demonstrate the potential of this new framework by considering different decision models in various realistic "what if" experiment scenarios. We implement these decision models to generate and study the evolution of the resulting AS graphs over time, and compare them against observed historical evolutionary features of the Internet at the AS level.

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Recently developed techniques have been very successful in accurately estimating intra-Autonomous System (AS) traffic matrices. These techniques rely on link measurements, flow measurements, or routing-related data to infer traffic demand between every pair of ingress-egress points of an AS. They also illustrate an inherent mismatch between data needed (e.g., ingress-egress demand) and data most readily available (e.g., link measurements). This mismatch is exacerbated when we try to estimate inter-AS traffic matrices, i.e., snapshots of Internet-wide traffic behavior over coarse time scale (a week or longer) between ASs. We present a method for modeling inter-AS traffic demand that relies exclusively on publicly available/obtainable measurements. We first perform extensive Internet-wide measurement experiments to infer the "business rationale" of individual ASs. We then use these business profiles to characterize individual ASs, classifying them by their "utility" into ASs providing Web hosting, residential access, and business access. We rank ASs by their utilities which drive our gravity-model based approach for generating inter-AS traffic demand. In a first attempt to validate our methodology, we test our inter-AS traffic generation method on an inferred Internet AS graph and present some preliminary findings about the resulting inter-AS traffic matrices.