The PALU model is a hybrid power law generative framework that decomposes Internet traffic graphs into three structural components: a preferential attachment core, degree one leaf nodes, and disconnected satellite clusters. Although the original PALU formulation provided a theoretical basis for this decomposition, it was never validated against real traffic data and was restricted to unweighted networks. This work presents the first empirical evaluation of PALU against real Internet traffic, using packet traces from the MAWI WIDE samplepoint F dataset. We introduce a weighted extension of the model incorporating strength degree scaling, and develop a packet-to-graph pipeline for parameter estimation across multiple observation window sizes. Results show that the power law degree exponent $α\approx 2.0$ remains stable across observation scales from $10^5$ to $3 \times 10^6$ packets, consistent with the PALU structural prediction. However, the satellite cluster parameter $λ$ and the weighted scaling parameter $θ$ exhibit systematic dependence on observation scale, and the Poisson star assumption for unattached components is formally rejected at all tested window sizes. These results indicate that PALU captures important aspects of Internet traffic structure but requires an explicit observation model to fully account for finite sampling effects.

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