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.