Measurement-based Modeling with Adaptive Sampling

摘要

To develop an accurate parametric model for network character is much difficult. We propose an Fitting-based Adaptive Sampling Methodology (FASM) trying to model some network metrics non-parametrically. The contributions of the paper are twofold: (1) Adopting Piecewise Linear Function Approximation scheme to provide more accurate approximation of the true metric model. (2) The statistical metric derived from the non-parametric model provides much more stable, lower variance and accurate estimation than other popular methodologies under the same sampling size. Experiments based on two measurement traces show that FASM dramatically reduces the number of samples while retaining the same approximating residual error than others.