This monograph explains my research in teletraffic modeling by using
two-parameter correlation function from the point of view of abstract
analysis in Hilbert spaces. Methodologically, the book utilizes the
extensions of fractional Gaussian noise (fGn) to study teletraffic
modeling such that the extensions with two parameters are more flexible
and accurate for traffic modeling than fGn, independent of the
generalized Cauchy process, which was recently noticed in stochastic
processes. The monograph is in the style of combining abstract analysis
of traffic with processing real-traffic data. It focuses on the
correlation form of traffic. For some fractal properties of traffic,
such as multi-fractal, readers may refer to other references or some
references in this book. This monograph may be a reference for
postgraduates, scholars, and engineers in computer science, as well as
those who are interested in traffic time series in applied statistics.