An Empirical Approach to Modeling Inter-AS Traffic Matrices


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.

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